DonorsChoose

DonorsChoose.org receives hundreds of thousands of project proposals each year for classroom projects in need of funding. Right now, a large number of volunteers is needed to manually screen each submission before it's approved to be posted on the DonorsChoose.org website.

Next year, DonorsChoose.org expects to receive close to 500,000 project proposals. As a result, there are three main problems they need to solve:

  • How to scale current manual processes and resources to screen 500,000 projects so that they can be posted as quickly and as efficiently as possible
  • How to increase the consistency of project vetting across different volunteers to improve the experience for teachers
  • How to focus volunteer time on the applications that need the most assistance

The goal of the competition is to predict whether or not a DonorsChoose.org project proposal submitted by a teacher will be approved, using the text of project descriptions as well as additional metadata about the project, teacher, and school. DonorsChoose.org can then use this information to identify projects most likely to need further review before approval.

About the DonorsChoose Data Set

The train.csv data set provided by DonorsChoose contains the following features:

Feature Description
project_id A unique identifier for the proposed project. Example: p036502
project_title Title of the project. Examples:
  • Art Will Make You Happy!
  • First Grade Fun
project_grade_category Grade level of students for which the project is targeted. One of the following enumerated values:
  • Grades PreK-2
  • Grades 3-5
  • Grades 6-8
  • Grades 9-12
project_subject_categories One or more (comma-separated) subject categories for the project from the following enumerated list of values:
  • Applied Learning
  • Care & Hunger
  • Health & Sports
  • History & Civics
  • Literacy & Language
  • Math & Science
  • Music & The Arts
  • Special Needs
  • Warmth

Examples:
  • Music & The Arts
  • Literacy & Language, Math & Science
school_state State where school is located (Two-letter U.S. postal code). Example: WY
project_subject_subcategories One or more (comma-separated) subject subcategories for the project. Examples:
  • Literacy
  • Literature & Writing, Social Sciences
project_resource_summary An explanation of the resources needed for the project. Example:
  • My students need hands on literacy materials to manage sensory needs!
project_essay_1 First application essay*
project_essay_2 Second application essay*
project_essay_3 Third application essay*
project_essay_4 Fourth application essay*
project_submitted_datetime Datetime when project application was submitted. Example: 2016-04-28 12:43:56.245
teacher_id A unique identifier for the teacher of the proposed project. Example: bdf8baa8fedef6bfeec7ae4ff1c15c56
teacher_prefix Teacher's title. One of the following enumerated values:
  • nan
  • Dr.
  • Mr.
  • Mrs.
  • Ms.
  • Teacher.
teacher_number_of_previously_posted_projects Number of project applications previously submitted by the same teacher. Example: 2

* See the section Notes on the Essay Data for more details about these features.

Additionally, the resources.csv data set provides more data about the resources required for each project. Each line in this file represents a resource required by a project:

Feature Description
id A project_id value from the train.csv file. Example: p036502
description Desciption of the resource. Example: Tenor Saxophone Reeds, Box of 25
quantity Quantity of the resource required. Example: 3
price Price of the resource required. Example: 9.95

Note: Many projects require multiple resources. The id value corresponds to a project_id in train.csv, so you use it as a key to retrieve all resources needed for a project:

The data set contains the following label (the value you will attempt to predict):

Label Description
project_is_approved A binary flag indicating whether DonorsChoose approved the project. A value of 0 indicates the project was not approved, and a value of 1 indicates the project was approved.

Notes on the Essay Data

    Prior to May 17, 2016, the prompts for the essays were as follows:
  • __project_essay_1:__ "Introduce us to your classroom"
  • __project_essay_2:__ "Tell us more about your students"
  • __project_essay_3:__ "Describe how your students will use the materials you're requesting"
  • __project_essay_3:__ "Close by sharing why your project will make a difference"
    Starting on May 17, 2016, the number of essays was reduced from 4 to 2, and the prompts for the first 2 essays were changed to the following:
  • __project_essay_1:__ "Describe your students: What makes your students special? Specific details about their background, your neighborhood, and your school are all helpful."
  • __project_essay_2:__ "About your project: How will these materials make a difference in your students' learning and improve their school lives?"

    • For all projects with project_submitted_datetime of 2016-05-17 and later, the values of project_essay_3 and project_essay_4 will be NaN.
In [1]:
%matplotlib inline
import warnings
warnings.filterwarnings("ignore")

import sqlite3
import pandas as pd
import numpy as np
import nltk
import string
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import TfidfVectorizer

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import confusion_matrix
from sklearn import metrics
from sklearn.metrics import roc_curve, auc
from nltk.stem.porter import PorterStemmer

import re
# Tutorial about Python regular expressions: https://pymotw.com/2/re/
import string
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.stem.wordnet import WordNetLemmatizer

from gensim.models import Word2Vec
from gensim.models import KeyedVectors
import pickle

from tqdm import tqdm
import os

from chart_studio.plotly import plotly
import plotly.offline as offline
import plotly.graph_objs as go
offline.init_notebook_mode()
from collections import Counter

1.1 Reading Data

In [2]:
project_data = pd.read_csv('train_data.csv')
resource_data = pd.read_csv('resources.csv')
In [3]:
print("Number of data points in train data", project_data.shape)
print('-'*50)
print("The attributes of data :", project_data.columns.values)

Number of data points in train data (109248, 17)
--------------------------------------------------
The attributes of data : ['Unnamed: 0' 'id' 'teacher_id' 'teacher_prefix' 'school_state'
 'project_submitted_datetime' 'project_grade_category'
 'project_subject_categories' 'project_subject_subcategories'
 'project_title' 'project_essay_1' 'project_essay_2' 'project_essay_3'
 'project_essay_4' 'project_resource_summary'
 'teacher_number_of_previously_posted_projects' 'project_is_approved']
In [4]:
print("Number of data points in train data", resource_data.shape)
print(resource_data.columns.values)
resource_data.head(2)

Number of data points in train data (1541272, 4)
['id' 'description' 'quantity' 'price']
Out[4]:
id description quantity price
0 p233245 LC652 - Lakeshore Double-Space Mobile Drying Rack 1 149.00
1 p069063 Bouncy Bands for Desks (Blue support pipes) 3 14.95
In [5]:
project_data=project_data.sample(n=50000)
project_data.shape
Out[5]:
(50000, 17)
In [6]:
project_data['project_is_approved'].value_counts()
Out[6]:
1    42481
0     7519
Name: project_is_approved, dtype: int64
In [7]:
# how to replace elements in list python: https://stackoverflow.com/a/2582163/4084039
cols = ['Date' if x=='project_submitted_datetime' else x for x in list(project_data.columns)]


#sort dataframe based on time pandas python: https://stackoverflow.com/a/49702492/4084039
project_data['Date'] = pd.to_datetime(project_data['project_submitted_datetime'])
project_data.drop('project_submitted_datetime', axis=1, inplace=True)
project_data.sort_values(by=['Date'], inplace=True)


# how to reorder columns pandas python: https://stackoverflow.com/a/13148611/4084039
project_data = project_data[cols]


project_data.head(2)
Out[7]:
Unnamed: 0 id teacher_id teacher_prefix school_state Date project_grade_category project_subject_categories project_subject_subcategories project_title project_essay_1 project_essay_2 project_essay_3 project_essay_4 project_resource_summary teacher_number_of_previously_posted_projects project_is_approved
55660 8393 p205479 2bf07ba08945e5d8b2a3f269b2b3cfe5 Mrs. CA 2016-04-27 00:27:36 Grades PreK-2 Math & Science Applied Sciences, Health & Life Science Engineering STEAM into the Primary Classroom I have been fortunate enough to use the Fairy ... My students come from a variety of backgrounds... Each month I try to do several science or STEM... It is challenging to develop high quality scie... My students need STEM kits to learn critical s... 53 1
473 100660 p234804 cbc0e38f522143b86d372f8b43d4cff3 Mrs. GA 2016-04-27 00:53:00 Grades PreK-2 Applied Learning Early Development Flexible Seating for Flexible Learning I recently read an article about giving studen... I teach at a low-income (Title 1) school. Ever... We need a classroom rug that we can use as a c... Benjamin Franklin once said, \"Tell me and I f... My students need flexible seating in the class... 2 1

1.2 preprocessing of project_subject_categories

In [8]:
print(project_data['project_subject_categories'].head(5))

55660                        Math & Science
473                        Applied Learning
49228                   Literacy & Language
72638    Applied Learning, Music & The Arts
7176       Math & Science, Applied Learning
Name: project_subject_categories, dtype: object
In [9]:
catogories = list(project_data['project_subject_categories'].values)
# remove special characters from list of strings python: https://stackoverflow.com/a/47301924/4084039

# https://www.geeksforgeeks.org/removing-stop-words-nltk-python/
# https://stackoverflow.com/questions/23669024/how-to-strip-a-specific-word-from-a-string
# https://stackoverflow.com/questions/8270092/remove-all-whitespace-in-a-string-in-python
cat_list = []
for i in catogories:
    temp = ""
    # consider we have text like this "Math & Science, Warmth, Care & Hunger"
    for j in i.split(','): # it will split it in three parts ["Math & Science", "Warmth", "Care & Hunger"]
        if 'The' in j.split(): # this will split each of the catogory based on space "Math & Science"=> "Math","&", "Science"
            j=j.replace('The','') # if we have the words "The" we are going to replace it with ''(i.e removing 'The')
        j = j.replace(' ','') # we are placeing all the ' '(space) with ''(empty) ex:"Math & Science"=>"Math&Science"
        temp+=j.strip()+" " #" abc ".strip() will return "abc", remove the trailing spaces
        temp = temp.replace('&','_') # we are replacing the & value into 
    cat_list.append(temp.strip())
    
project_data['clean_categories'] = cat_list
project_data.drop(['project_subject_categories'], axis=1, inplace=True)

from collections import Counter
my_counter = Counter()
for word in project_data['clean_categories'].values:
    my_counter.update(word.split())

cat_dict = dict(my_counter)
sorted_cat_dict = dict(sorted(cat_dict.items(), key=lambda kv: kv[1]))
In [10]:
print(project_data['clean_categories'].head(5))

55660                    Math_Science
473                   AppliedLearning
49228               Literacy_Language
72638      AppliedLearning Music_Arts
7176     Math_Science AppliedLearning
Name: clean_categories, dtype: object

1.3 preprocessing of project_subject_subcategories

In [11]:
print(project_data['project_subject_subcategories'].head(5))

55660    Applied Sciences, Health & Life Science
473                            Early Development
49228                                   Literacy
72638               Extracurricular, Visual Arts
7176         Applied Sciences, Early Development
Name: project_subject_subcategories, dtype: object
In [12]:
sub_catogories = list(project_data['project_subject_subcategories'].values)
# remove special characters from list of strings python: https://stackoverflow.com/a/47301924/4084039

# https://www.geeksforgeeks.org/removing-stop-words-nltk-python/
# https://stackoverflow.com/questions/23669024/how-to-strip-a-specific-word-from-a-string
# https://stackoverflow.com/questions/8270092/remove-all-whitespace-in-a-string-in-python

sub_cat_list = []
for i in sub_catogories:
    temp = ""
    # consider we have text like this "Math & Science, Warmth, Care & Hunger"
    for j in i.split(','): # it will split it in three parts ["Math & Science", "Warmth", "Care & Hunger"]
        if 'The' in j.split(): # this will split each of the catogory based on space "Math & Science"=> "Math","&", "Science"
            j=j.replace('The','') # if we have the words "The" we are going to replace it with ''(i.e removing 'The')
        j = j.replace(' ','') # we are placeing all the ' '(space) with ''(empty) ex:"Math & Science"=>"Math&Science"
        temp +=j.strip()+" "#" abc ".strip() will return "abc", remove the trailing spaces
        temp = temp.replace('&','_')
    sub_cat_list.append(temp.strip())

project_data['clean_subcategories'] = sub_cat_list
project_data.drop(['project_subject_subcategories'], axis=1, inplace=True)

# count of all the words in corpus python: https://stackoverflow.com/a/22898595/4084039
my_counter = Counter()
for word in project_data['clean_subcategories'].values:
    my_counter.update(word.split())
    
sub_cat_dict = dict(my_counter)
sorted_sub_cat_dict = dict(sorted(sub_cat_dict.items(), key=lambda kv: kv[1]))
In [13]:
print(project_data['clean_subcategories'].head(5))

55660    AppliedSciences Health_LifeScience
473                        EarlyDevelopment
49228                              Literacy
72638            Extracurricular VisualArts
7176       AppliedSciences EarlyDevelopment
Name: clean_subcategories, dtype: object

1.4 preprocessing of school_state

In [14]:
my_counter = Counter()
for word in project_data['school_state'].values:
    my_counter.update(word.split())
    
state_dict = dict(my_counter)
sorted_state_dict = dict(sorted(state_dict.items(), key=lambda kv: kv[1]))
In [15]:
print(project_data['school_state'].head(5))

55660    CA
473      GA
49228    IL
72638    OH
7176     OH
Name: school_state, dtype: object

1.5 preprocessing of project_grade_category

In [16]:
project_data['project_grade_category'].value_counts()
Out[16]:
Grades PreK-2    20268
Grades 3-5       16955
Grades 6-8        7776
Grades 9-12       5001
Name: project_grade_category, dtype: int64
In [17]:
#https://stackoverflow.com/questions/36383821/pandas-dataframe-apply-function-to-column-strings-based-on-other-column-value
project_data['project_grade_category'] = project_data['project_grade_category'].str.replace(' ','_')
project_data['project_grade_category'] = project_data['project_grade_category'].str.replace('-','_')
project_data['project_grade_category'] = project_data['project_grade_category'].str.lower()
project_data['project_grade_category'].value_counts()
Out[17]:
grades_prek_2    20268
grades_3_5       16955
grades_6_8        7776
grades_9_12       5001
Name: project_grade_category, dtype: int64
In [18]:
my_counter = Counter()
for word in project_data['project_grade_category'].values:
    my_counter.update(word.split())

grade_dict = dict(my_counter)
sorted_grade_dict = dict(sorted(grade_dict.items(), key=lambda kv: kv[1]))
In [19]:
print(sorted_grade_dict)

{'grades_9_12': 5001, 'grades_6_8': 7776, 'grades_3_5': 16955, 'grades_prek_2': 20268}

1.6 preprocessing of teacher_prefix

In [20]:
project_data['teacher_prefix'].value_counts()
Out[20]:
Mrs.       26049
Ms.        17952
Mr.         4872
Teacher     1120
Dr.            6
Name: teacher_prefix, dtype: int64
In [21]:
project_data['teacher_prefix']=project_data['teacher_prefix'].fillna('Mrs.')
project_data['teacher_prefix'] = project_data['teacher_prefix'].str.replace('-','_')
project_data['teacher_prefix'] = project_data['teacher_prefix'].str.replace('.',' ')
project_data['teacher_prefix'] = project_data['teacher_prefix'].str.lower()
project_data['teacher_prefix'].value_counts()
Out[21]:
mrs        26050
ms         17952
mr          4872
teacher     1120
dr             6
Name: teacher_prefix, dtype: int64
In [22]:
my_counter = Counter()
for word in project_data['teacher_prefix'].values:
    my_counter.update(word.split())

teacher_dict = dict(my_counter)
sorted_teacher_dict = dict(sorted(teacher_dict.items(), key=lambda kv: kv[1]))
In [23]:
print(project_data['teacher_prefix'].head(5))

55660    mrs 
473      mrs 
49228     ms 
72638     mr 
7176     mrs 
Name: teacher_prefix, dtype: object

1.7 Text preprocessing

In [24]:
# merge two column text dataframe: 
project_data["essay"] = project_data["project_essay_1"].map(str) +\
                        project_data["project_essay_2"].map(str) + \
                        project_data["project_essay_3"].map(str) + \
                        project_data["project_essay_4"].map(str)
In [25]:
project_data.head(2)
Out[25]:
Unnamed: 0 id teacher_id teacher_prefix school_state Date project_grade_category project_title project_essay_1 project_essay_2 project_essay_3 project_essay_4 project_resource_summary teacher_number_of_previously_posted_projects project_is_approved clean_categories clean_subcategories essay
55660 8393 p205479 2bf07ba08945e5d8b2a3f269b2b3cfe5 mrs CA 2016-04-27 00:27:36 grades_prek_2 Engineering STEAM into the Primary Classroom I have been fortunate enough to use the Fairy ... My students come from a variety of backgrounds... Each month I try to do several science or STEM... It is challenging to develop high quality scie... My students need STEM kits to learn critical s... 53 1 Math_Science AppliedSciences Health_LifeScience I have been fortunate enough to use the Fairy ...
473 100660 p234804 cbc0e38f522143b86d372f8b43d4cff3 mrs GA 2016-04-27 00:53:00 grades_prek_2 Flexible Seating for Flexible Learning I recently read an article about giving studen... I teach at a low-income (Title 1) school. Ever... We need a classroom rug that we can use as a c... Benjamin Franklin once said, \"Tell me and I f... My students need flexible seating in the class... 2 1 AppliedLearning EarlyDevelopment I recently read an article about giving studen...
In [26]:
#### 1.4.2.3 Using Pretrained Models: TFIDF weighted W2V
In [27]:
# printing some random reviews
print(project_data['essay'].values[0])
print("="*50)
print(project_data['essay'].values[150])
print("="*50)
print(project_data['essay'].values[1000])
print("="*50)
print(project_data['essay'].values[20000])
print("="*50)
print(project_data['essay'].values[49999])
print("="*50)

I have been fortunate enough to use the Fairy Tale STEM kits in my classroom as well as the STEM journals, which my students really enjoyed.  I would love to implement more of the Lakeshore STEM kits in my classroom for the next school year as they provide excellent and engaging STEM lessons.My students come from a variety of backgrounds, including language and socioeconomic status.  Many of them don't have a lot of experience in science and engineering and these kits give me the materials to provide these exciting opportunities for my students.Each month I try to do several science or STEM/STEAM projects.  I would use the kits and robot to help guide my science instruction in engaging and meaningful ways.  I can adapt the kits to my current language arts pacing guide where we already teach some of the material in the kits like tall tales (Paul Bunyan) or Johnny Appleseed.  The following units will be taught in the next school year where I will implement these kits: magnets, motion, sink vs. float, robots.  I often get to these units and don't know If I am teaching the right way or using the right materials.    The kits will give me additional ideas, strategies, and lessons to prepare my students in science.It is challenging to develop high quality science activities.  These kits give me the materials I need to provide my students with science activities that will go along with the curriculum in my classroom.  Although I have some things (like magnets) in my classroom, I don't know how to use them effectively.  The kits will provide me with the right amount of materials and show me how to use them in an appropriate way.
==================================================
Clock ticking. Lights flickering. Perfume. New clothes. These daily encounters often go unnoticed by most, but these, and many other experiences can cause sensory overload for students with autism or sensory processing disorders.I have the joy of teaching 12 students in a 2nd-4th grade special education classroom setting which includes children with mild intellectual disabilities, autism, and other handicapping impairments. Each day provides a new learning experience not only for my students, but for me as well!Sensory overload occurs when an individual has difficulty processing everyday sensory information.  Sensory overload causes stress and anxiety, which can lead to withdrawal, challenging behaviors, and even meltdowns. Students can be over- and under-stimulated. That’s where a sensory room comes in. Sensory rooms are designed to give individuals with sensory processing disorders the opportunity to learn to relax and self-regulate through learning and practicing stress management techniques, and through sensory integration.  A sensory room provides activities to calm or stimulate the body and mind through each of the senses. Research has shown that individuals who participate in sensory integration show an increase in calmness, concentration, focus,and alertness, as well as a decrease in aggression. My desire is to create a safe environment that will provide opportunities for my students to meet their visual, olfactory, oral-motor, proprioception, tactile, and auditory needs.This sensory room will be used by my students and the three other special education classes at our school. It will also be available for any student who needs to have their sensory needs met. By having my project supported I will be able to turn an empty classroom into a sensory room that will allow me to meet the sensory input needs of my students, which in turn will allow me to greater meet their academic needs as well. Thank you for supporting my classroom and my students!
==================================================
My classroom is a place where students feel like a family and we build relationships with one another.  My students develop a love a reading through the books I incorporate within my classroom lessons.  They run to the library and find books by the same author or same topic and begin their journey.My students are a diverse group.  They come in with many different backgrounds and experiences.  They are eager to learn and excited to be at school.  Their academic abilities range in different areas. They are interested in Lego's, Littlest Pet Shop, and utilizing technology. They are competitive with each other and enjoy playing games.  My school is a large K-8 elementary school.  We are located in a low socioeconomic area which means 70% of our students receive free or reduced lunch.  Each grade level has at least three to four classrooms.  Our class sizes range from 18-higher.  We are located in a small mountain community where everyone knows one another.  I would describe my school as a place where students are put first.  We encourage parental involvement at home and at school.  I would say we are a close school district.One of my main goals at the beginning of the school year is to build classroom community.  We would meet on our classroom rug daily. The students would have a spot in the room for our novel reading where we could gather together instead of sitting at our desks.  Being able to gather at one spot in the room would allow for the students to have a deeper discussion about the novel, be able to answer questions about main idea, cause and effect, and other literacy concepts incorporated into a novel study. The students will use the rug to partner read which would build fluency.  At the end of 3rd grade they should be reading 100 wpm.   The students would also use the rug for partner/group work.  This area would provide a place for them to work collaboratively with one another practicing  math concepts such as fractions or place value.  Learning through collaboration is an important skill and allows students who may not understand quite as easily through whole group instruction.A  rug would improve my classroom by providing an opportunity to build classroom community.  My students would be able to meet as a group to do activities, work collaboratively practicing important literacy and math concepts, and have deeper discussions.  Students would build fluency, comprehension, and vocabulary skills.  They would build an understanding in math concepts that lay the academic foundation for later in their academic career.
==================================================
I have so much to say about my students I don't even know where to begin.  They love life, they bound into school with unmatched energy, and they have the best smiles around.  Everyday we laugh, learn, and grow together in reading, writing, math, science, social studies, and LIFE.  \r\nDespite the many hardships life throws these kids they persevere with a passion I learn from every day.   Our school is a Title 1 school and over 70% of our students qualify for free or reduced lunch.  I rarely see these kids quit and when things get tough they back up and try again.I know my third graders very well, I was lucky enough to teach them in second grade.  I know their learning styles, their habits, their stressors, and what they need on a daily basis to be successful.  They like to move and fidget a lot but we've got important work that needs to be done.  \r\nAt every chance I get we engage in a brain break but while their brains are working hard I would like to give them the opportunity to work their bodies, too by utilizing bands on their chairs or seats.  At the same time, we need our classroom to be a safe and calming environment.  Unfortunately, I cannot control what happens when they leave my room but when they are there I want them to be comfortable.  I will use the light covers to create that space for them.nannan
==================================================
I teach first grade in a Title I school. Although my students may live in an impoverished neighborhood, my students are vibrant and will brighten up your day! They can be an energetic bunch! They are hard workers and love to show it to anyone who walks into our classroom or whoever they see in the hallway! \r\n\r\nThey are an amazing group of students with different abilities and personalities. They are busting at the seams to learn.  They are excited to learn and grow.\"Love of beauty is taste. The creation of beauty is art.\"\r\n~Ralph Waldo Emerson\r\n\r\nMy students are hard working First Grade students.  I want to brighten up their work by making their centers colorful.  I will use the ink and colorful paper to create centers that will keep them engage.  I will laminate their centers to make my centers eco-friendly.  My students will be using the ink and colorful paper to print their projects and papers.  My students write stories and draw their illustrations.  The ink and paper will allow my students to use their technology skills to type their stories and draw their illustrations on the computer to create their books.  This project will connect multiple subjects and allow students of various abilities to participate and demonstrate their work in a variety of ways.\r\nThis project will enhance my students' work.  They work hard and would love to display their work around the classroom and in the school.  The colorful display will brighten up our school.nannan
==================================================
In [28]:
# https://stackoverflow.com/a/47091490/4084039
import re

def decontracted(phrase):
    # specific
    phrase = re.sub(r"won't", "will not", phrase)
    phrase = re.sub(r"can\'t", "can not", phrase)

    # general
    phrase = re.sub(r"n\'t", " not", phrase)
    phrase = re.sub(r"\'re", " are", phrase)
    phrase = re.sub(r"\'s", " is", phrase)
    phrase = re.sub(r"\'d", " would", phrase)
    phrase = re.sub(r"\'ll", " will", phrase)
    phrase = re.sub(r"\'t", " not", phrase)
    phrase = re.sub(r"\'ve", " have", phrase)
    phrase = re.sub(r"\'m", " am", phrase)
    return phrase
In [29]:
sent = decontracted(project_data['essay'].values[20000])
print(sent)
print("="*50)

I have so much to say about my students I do not even know where to begin.  They love life, they bound into school with unmatched energy, and they have the best smiles around.  Everyday we laugh, learn, and grow together in reading, writing, math, science, social studies, and LIFE.  \r\nDespite the many hardships life throws these kids they persevere with a passion I learn from every day.   Our school is a Title 1 school and over 70% of our students qualify for free or reduced lunch.  I rarely see these kids quit and when things get tough they back up and try again.I know my third graders very well, I was lucky enough to teach them in second grade.  I know their learning styles, their habits, their stressors, and what they need on a daily basis to be successful.  They like to move and fidget a lot but we have got important work that needs to be done.  \r\nAt every chance I get we engage in a brain break but while their brains are working hard I would like to give them the opportunity to work their bodies, too by utilizing bands on their chairs or seats.  At the same time, we need our classroom to be a safe and calming environment.  Unfortunately, I cannot control what happens when they leave my room but when they are there I want them to be comfortable.  I will use the light covers to create that space for them.nannan
==================================================
In [30]:
# \r \n \t remove from string python: http://texthandler.com/info/remove-line-breaks-python/
sent = sent.replace('\\r', ' ')
sent = sent.replace('\\"', ' ')
sent = sent.replace('\\n', ' ')
print(sent)

I have so much to say about my students I do not even know where to begin.  They love life, they bound into school with unmatched energy, and they have the best smiles around.  Everyday we laugh, learn, and grow together in reading, writing, math, science, social studies, and LIFE.    Despite the many hardships life throws these kids they persevere with a passion I learn from every day.   Our school is a Title 1 school and over 70% of our students qualify for free or reduced lunch.  I rarely see these kids quit and when things get tough they back up and try again.I know my third graders very well, I was lucky enough to teach them in second grade.  I know their learning styles, their habits, their stressors, and what they need on a daily basis to be successful.  They like to move and fidget a lot but we have got important work that needs to be done.    At every chance I get we engage in a brain break but while their brains are working hard I would like to give them the opportunity to work their bodies, too by utilizing bands on their chairs or seats.  At the same time, we need our classroom to be a safe and calming environment.  Unfortunately, I cannot control what happens when they leave my room but when they are there I want them to be comfortable.  I will use the light covers to create that space for them.nannan
In [31]:
#remove spacial character: https://stackoverflow.com/a/5843547/4084039
sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
print(sent)

I have so much to say about my students I do not even know where to begin They love life they bound into school with unmatched energy and they have the best smiles around Everyday we laugh learn and grow together in reading writing math science social studies and LIFE Despite the many hardships life throws these kids they persevere with a passion I learn from every day Our school is a Title 1 school and over 70 of our students qualify for free or reduced lunch I rarely see these kids quit and when things get tough they back up and try again I know my third graders very well I was lucky enough to teach them in second grade I know their learning styles their habits their stressors and what they need on a daily basis to be successful They like to move and fidget a lot but we have got important work that needs to be done At every chance I get we engage in a brain break but while their brains are working hard I would like to give them the opportunity to work their bodies too by utilizing bands on their chairs or seats At the same time we need our classroom to be a safe and calming environment Unfortunately I cannot control what happens when they leave my room but when they are there I want them to be comfortable I will use the light covers to create that space for them nannan
In [32]:
# https://gist.github.com/sebleier/554280
# we are removing the words from the stop words list: 'no', 'nor', 'not'
stopwords= ['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', "you're", "you've",\
            "you'll", "you'd", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', \
            'she', "she's", 'her', 'hers', 'herself', 'it', "it's", 'its', 'itself', 'they', 'them', 'their',\
            'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', "that'll", 'these', 'those', \
            'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', \
            'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', \
            'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after',\
            'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further',\
            'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more',\
            'most', 'other', 'some', 'such', 'only', 'own', 'same', 'so', 'than', 'too', 'very', \
            's', 't', 'can', 'will', 'just', 'don', "don't", 'should', "should've", 'now', 'd', 'll', 'm', 'o', 're', \
            've', 'y', 'ain', 'aren', "aren't", 'couldn', "couldn't", 'didn', "didn't", 'doesn', "doesn't", 'hadn',\
            "hadn't", 'hasn', "hasn't", 'haven', "haven't", 'isn', "isn't", 'ma', 'mightn', "mightn't", 'mustn',\
            "mustn't", 'needn', "needn't", 'shan', "shan't", 'shouldn', "shouldn't", 'wasn', "wasn't", 'weren', "weren't", \
            'won', "won't", 'wouldn', "wouldn't"]
In [33]:
# Combining all the above stundents 
from tqdm import tqdm
preprocessed_essays = []
# tqdm is for printing the status bar
for sentance in tqdm(project_data['essay'].values):
    sent = decontracted(sentance)
    sent = sent.replace('\\r', ' ')
    sent = sent.replace('\\"', ' ')
    sent = sent.replace('\\n', ' ')
    sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
    # https://gist.github.com/sebleier/554280
    sent = ' '.join(e for e in sent.split() if e not in stopwords)
    preprocessed_essays.append(sent.lower().strip())

100%|███████████████████████████████████████████████████████████████████████████| 50000/50000 [01:06<00:00, 755.65it/s]
In [34]:
# after preprocesing
preprocessed_essays[20000]
Out[34]:
'i much say students i not even know begin they love life bound school unmatched energy best smiles around everyday laugh learn grow together reading writing math science social studies life despite many hardships life throws kids persevere passion i learn every day our school title 1 school 70 students qualify free reduced lunch i rarely see kids quit things get tough back try i know third graders well i lucky enough teach second grade i know learning styles habits stressors need daily basis successful they like move fidget lot got important work needs done at every chance i get engage brain break brains working hard i would like give opportunity work bodies utilizing bands chairs seats at time need classroom safe calming environment unfortunately i cannot control happens leave room i want comfortable i use light covers create space nannan'

1.8 Preprocessing of `project_title`

In [35]:
# similarly you can preprocess the titles also
preprocessed_titles = []
# tqdm is for printing the status bar
for sentence in tqdm(project_data['project_title'].values):
    sent = decontracted(sentence)
    sent = sent.replace('\\r', ' ')
    sent = sent.replace('\\"', ' ')
    sent = sent.replace('\\n', ' ')
    sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
    # https://gist.github.com/sebleier/554280
    sent = ' '.join(e for e in sent.split() if e not in stopwords)
    preprocessed_titles.append(sent.lower().strip())

100%|█████████████████████████████████████████████████████████████████████████| 50000/50000 [00:03<00:00, 16607.82it/s]
In [36]:
print(45660,preprocessed_titles[45660])

45660 our world view
In [37]:
project_data['project_title']=preprocessed_titles
In [38]:
print(45660,project_data['project_title'].iloc[45660])

45660 our world view

Sentiment Analysis of essays

In [39]:
import nltk
nltk.downloader.download('vader_lexicon')
from nltk.sentiment.vader import SentimentIntensityAnalyzer
analyser = SentimentIntensityAnalyzer()

neg = []
pos = []
neu = []
compound = []

for a in tqdm(project_data["essay"]) :
    b = analyser.polarity_scores(a)['neg']
    c = analyser.polarity_scores(a)['pos']
    d = analyser.polarity_scores(a)['neu']
    e = analyser.polarity_scores(a)['compound']
    neg.append(b)
    pos.append(c)
    neu.append(d)
    compound.append(e)

[nltk_data] Downloading package vader_lexicon to C:\Users\ABHISHEK
[nltk_data]     SINGH\AppData\Roaming\nltk_data...
[nltk_data]   Package vader_lexicon is already up-to-date!
100%|████████████████████████████████████████████████████████████████████████████| 50000/50000 [21:48<00:00, 38.21it/s]
In [40]:
project_data["pos"] = pos
In [41]:
project_data["neg"] = neg
In [42]:
project_data["neu"] = neu
In [43]:
project_data["compound"] = compound

Number of Words in Title

In [44]:
title_word_count = []

for a in project_data["project_title"] :
    b = len(a.split())
    title_word_count.append(b)

project_data["title_word_count"] = title_word_count

Number of Words in Essays

In [45]:
essay_word_count = []

for a in project_data["essay"] :
    b = len(a.split())
    essay_word_count.append(b)

project_data["essay_word_count"] = essay_word_count

1.9 Preparing data for models

In [46]:
project_data.columns
Out[46]:
Index(['Unnamed: 0', 'id', 'teacher_id', 'teacher_prefix', 'school_state',
       'Date', 'project_grade_category', 'project_title', 'project_essay_1',
       'project_essay_2', 'project_essay_3', 'project_essay_4',
       'project_resource_summary',
       'teacher_number_of_previously_posted_projects', 'project_is_approved',
       'clean_categories', 'clean_subcategories', 'essay', 'pos', 'neg', 'neu',
       'compound', 'title_word_count', 'essay_word_count'],
      dtype='object')

we are going to consider

   - school_state : categorical data
   - clean_categories : categorical data
   - clean_subcategories : categorical data
   - project_grade_category : categorical data
   - teacher_prefix : categorical data

   - project_title : text data
   - text : text data
   - project_resource_summary: text data (optinal)

   - quantity : numerical (optinal)
   - teacher_number_of_previously_posted_projects : numerical
   - price : numerical

Assignment 5: Logistic Regression

  1. [Task-1] Logistic Regression(either SGDClassifier with log loss, or LogisticRegression) on these feature sets
    • Set 1: categorical, numerical features + project_title(BOW) + preprocessed_eassay (`BOW with bi-grams` with `min_df=10` and `max_features=5000`)
    • Set 2: categorical, numerical features + project_title(TFIDF)+ preprocessed_eassay (`TFIDF with bi-grams` with `min_df=10` and `max_features=5000`)
    • Set 3: categorical, numerical features + project_title(AVG W2V)+ preprocessed_eassay (AVG W2V)
    • Set 4: categorical, numerical features + project_title(TFIDF W2V)+ preprocessed_essay (TFIDF W2V)

  2. Hyper paramter tuning (find best hyper parameters corresponding the algorithm that you choose)
    • Find the best hyper parameter which will give the maximum AUC value
    • Find the best hyper paramter using k-fold cross validation or simple cross validation data
    • Use gridsearch cv or randomsearch cv or you can also write your own for loops to do this task of hyperparameter tuning

  3. Representation of results
    • You need to plot the performance of model both on train data and cross validation data for each hyper parameter, like shown in the figure.
    • Once after you found the best hyper parameter, you need to train your model with it, and find the AUC on test data and plot the ROC curve on both train and test.
    • Along with plotting ROC curve, you need to print the confusion matrix with predicted and original labels of test data points. Please visualize your confusion matrices using seaborn heatmaps.

  4. [Task-2] Apply Logistic Regression on the below feature set Set 5 by finding the best hyper parameter as suggested in step 2 and step 3.
  5. Consider these set of features Set 5 :
    • school_state : categorical data
    • clean_categories : categorical data
    • clean_subcategories : categorical data
    • project_grade_category :categorical data
    • teacher_prefix : categorical data
    • quantity : numerical data
    • teacher_number_of_previously_posted_projects : numerical data
    • price : numerical data
    • sentiment score's of each of the essay : numerical data
    • number of words in the title : numerical data
    • number of words in the combine essays : numerical data
    And apply the Logistic regression on these features by finding the best hyper paramter as suggested in step 2 and step 3

  6. Conclusion

Note: Data Leakage

  1. There will be an issue of data-leakage if you vectorize the entire data and then split it into train/cv/test.
  2. To avoid the issue of data-leakage, make sure to split your data first and then vectorize it.
  3. While vectorizing your data, apply the method fit_transform() on you train data, and apply the method transform() on cv/test data.
  4. For more details please go through this link.

2. Logistic Regression

2.1 Splitting data into Train and cross validation(or test): Stratified Sampling

In [47]:
y = project_data['project_is_approved']
print(y.shape)

(50000,)
In [48]:
project_data.drop(['project_is_approved'],axis=1,inplace=True)
In [49]:
X=project_data
print(X.shape)

(50000, 23)
In [50]:
#train test split
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, stratify=y)
X_train, X_cv, y_train, y_cv = train_test_split(X_train, y_train, test_size=0.33, stratify=y_train)

2.2 Make Data Model Ready: encoding numerical, categorical features

In [51]:
print(X_train.shape, y_train.shape)
print(X_cv.shape, y_cv.shape)
print(X_test.shape, y_test.shape)
print("="*100)

(22445, 23) (22445,)
(11055, 23) (11055,)
(16500, 23) (16500,)
====================================================================================================

Encoding of Text Data

In [52]:
from sklearn.feature_extraction.text import CountVectorizer

BOW of Essay

In [53]:
vectorizer = CountVectorizer(min_df=10,ngram_range=(2,2), max_features=5000)
In [54]:
vectorizer.fit(X_train['essay'].values) # fit has to happen only on train data
Out[54]:
CountVectorizer(analyzer='word', binary=False, decode_error='strict',
                dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',
                lowercase=True, max_df=1.0, max_features=5000, min_df=10,
                ngram_range=(2, 2), preprocessor=None, stop_words=None,
                strip_accents=None, token_pattern='(?u)\\b\\w\\w+\\b',
                tokenizer=None, vocabulary=None)
In [55]:
# we use the fitted CountVectorizer to convert the text to vector
X_train_essay_bow = vectorizer.transform(X_train['essay'].values)
In [56]:
X_cv_essay_bow = vectorizer.transform(X_cv['essay'].values)
In [57]:
X_test_essay_bow = vectorizer.transform(X_test['essay'].values)
In [58]:
print("After vectorizations")
print(X_train_essay_bow.shape, y_train.shape)
print(X_cv_essay_bow.shape, y_cv.shape)
print(X_test_essay_bow.shape, y_test.shape)
print("="*100)

After vectorizations
(22445, 5000) (22445,)
(11055, 5000) (11055,)
(16500, 5000) (16500,)
====================================================================================================

BOW of Title

In [59]:
vectorizer = CountVectorizer(min_df=10,ngram_range=(1,4), max_features=5000)
In [60]:
vectorizer.fit(X_train['project_title'].values) # fit has to happen only on train data
Out[60]:
CountVectorizer(analyzer='word', binary=False, decode_error='strict',
                dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',
                lowercase=True, max_df=1.0, max_features=5000, min_df=10,
                ngram_range=(1, 4), preprocessor=None, stop_words=None,
                strip_accents=None, token_pattern='(?u)\\b\\w\\w+\\b',
                tokenizer=None, vocabulary=None)
In [61]:
# we use the fitted CountVectorizer to convert the text to vector
X_train_title_bow = vectorizer.transform(X_train['project_title'].values)
In [62]:
X_cv_title_bow = vectorizer.transform(X_cv['project_title'].values)
In [63]:
X_test_title_bow = vectorizer.transform(X_test['project_title'].values)
In [64]:
print("After vectorizations")
print(X_train_title_bow.shape, y_train.shape)
print(X_cv_title_bow.shape, y_cv.shape)
print(X_test_title_bow.shape, y_test.shape)
print("="*100)

After vectorizations
(22445, 2010) (22445,)
(11055, 2010) (11055,)
(16500, 2010) (16500,)
====================================================================================================

TFIDF of Essay

In [65]:
vectorizer = TfidfVectorizer(min_df=10,ngram_range=(2,2), max_features=5000)
In [66]:
vectorizer.fit(X_train['essay'].values) # fit has to happen only on train data
Out[66]:
TfidfVectorizer(analyzer='word', binary=False, decode_error='strict',
                dtype=<class 'numpy.float64'>, encoding='utf-8',
                input='content', lowercase=True, max_df=1.0, max_features=5000,
                min_df=10, ngram_range=(2, 2), norm='l2', preprocessor=None,
                smooth_idf=True, stop_words=None, strip_accents=None,
                sublinear_tf=False, token_pattern='(?u)\\b\\w\\w+\\b',
                tokenizer=None, use_idf=True, vocabulary=None)
In [67]:
# we use the fitted CountVectorizer to convert the text to vector
X_train_essay_tfidf = vectorizer.transform(X_train['essay'].values)
In [68]:
X_cv_essay_tfidf = vectorizer.transform(X_cv['essay'].values)
In [69]:
X_test_essay_tfidf = vectorizer.transform(X_test['essay'].values)
In [70]:
print("After vectorizations")
print(X_train_essay_tfidf.shape, y_train.shape)
print(X_cv_essay_tfidf.shape, y_cv.shape)
print(X_test_essay_tfidf.shape, y_test.shape)
print("="*100)

After vectorizations
(22445, 5000) (22445,)
(11055, 5000) (11055,)
(16500, 5000) (16500,)
====================================================================================================

TFIDF of Title

In [71]:
vectorizer = TfidfVectorizer(min_df=10,ngram_range=(1,4), max_features=5000)
In [72]:
vectorizer.fit(X_train['project_title'].values) # fit has to happen only on train data
Out[72]:
TfidfVectorizer(analyzer='word', binary=False, decode_error='strict',
                dtype=<class 'numpy.float64'>, encoding='utf-8',
                input='content', lowercase=True, max_df=1.0, max_features=5000,
                min_df=10, ngram_range=(1, 4), norm='l2', preprocessor=None,
                smooth_idf=True, stop_words=None, strip_accents=None,
                sublinear_tf=False, token_pattern='(?u)\\b\\w\\w+\\b',
                tokenizer=None, use_idf=True, vocabulary=None)
In [73]:
# we use the fitted CountVectorizer to convert the text to vector
X_train_title_tfidf = vectorizer.transform(X_train['project_title'].values)
In [74]:
X_cv_title_tfidf = vectorizer.transform(X_cv['project_title'].values)
In [75]:
X_test_title_tfidf = vectorizer.transform(X_test['project_title'].values)
In [76]:
print("After vectorizations")
print(X_train_title_tfidf.shape, y_train.shape)
print(X_cv_title_tfidf.shape, y_cv.shape)
print(X_test_title_tfidf.shape, y_test.shape)
print("="*100)

After vectorizations
(22445, 2010) (22445,)
(11055, 2010) (11055,)
(16500, 2010) (16500,)
====================================================================================================

Avg W2V of Essay

In [77]:
# stronging variables into pickle files python: http://www.jessicayung.com/how-to-use-pickle-to-save-and-load-variables-in-python/
# make sure you have the glove_vectors file
with open('glove_vectors', 'rb') as f:
    model = pickle.load(f,encoding = "ISO-8859-1")
    glove_words =  set(model.keys())
In [78]:
# average Word2Vec
# compute average word2vec for each essay.
avg_w2v_essay_train = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(X_train['essay'].values): # for each review/sentence
    vector = np.zeros(300) # as word vectors are of zero length
    cnt_words =0; # num of words with a valid vector in the sentence/review
    for word in sentence.split(): # for each word in a review/sentence
        if word in glove_words:
            vector += model[word]
            cnt_words += 1
    if cnt_words != 0:
        vector /= cnt_words
    avg_w2v_essay_train.append(vector)

print(len(avg_w2v_essay_train))
print(len(avg_w2v_essay_train[0]))
print(type(avg_w2v_essay_train))

100%|██████████████████████████████████████████████████████████████████████████| 22445/22445 [00:21<00:00, 1027.50it/s]

22445
300
<class 'list'>
In [79]:
# average Word2Vec
# compute average word2vec for each essay.
avg_w2v_essay_cv = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(X_cv['essay'].values): # for each review/sentence
    vector = np.zeros(300) # as word vectors are of zero length
    cnt_words =0; # num of words with a valid vector in the sentence/review
    for word in sentence.split(): # for each word in a review/sentence
        if word in glove_words:
            vector += model[word]
            cnt_words += 1
    if cnt_words != 0:
        vector /= cnt_words
    avg_w2v_essay_cv.append(vector)

print(len(avg_w2v_essay_cv))
print(len(avg_w2v_essay_cv[0]))
print(type(avg_w2v_essay_cv))

100%|███████████████████████████████████████████████████████████████████████████| 11055/11055 [00:11<00:00, 963.86it/s]

11055
300
<class 'list'>
In [80]:
# average Word2Vec
# compute average word2vec for each essay.
avg_w2v_essay_test = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(X_test['essay'].values): # for each review/sentence
    vector = np.zeros(300) # as word vectors are of zero length
    cnt_words =0; # num of words with a valid vector in the sentence/review
    for word in sentence.split(): # for each word in a review/sentence
        if word in glove_words:
            vector += model[word]
            cnt_words += 1
    if cnt_words != 0:
        vector /= cnt_words
    avg_w2v_essay_test.append(vector)

print(len(avg_w2v_essay_test))
print(len(avg_w2v_essay_test[0]))
print(type(avg_w2v_essay_test))

100%|██████████████████████████████████████████████████████████████████████████| 16500/16500 [00:16<00:00, 1013.56it/s]

16500
300
<class 'list'>

Avg W2V of Title

In [81]:
# average Word2Vec
# compute average word2vec for each essay.
avg_w2v_title_train = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(X_train['project_title'].values): # for each review/sentence
    vector = np.zeros(300) # as word vectors are of zero length
    cnt_words =0; # num of words with a valid vector in the sentence/review
    for word in sentence.split(): # for each word in a review/sentence
        if word in glove_words:
            vector += model[word]
            cnt_words += 1
    if cnt_words != 0:
        vector /= cnt_words
    avg_w2v_title_train.append(vector)

print(len(avg_w2v_title_train))
print(len(avg_w2v_title_train[0]))
print(type(avg_w2v_title_train))

100%|█████████████████████████████████████████████████████████████████████████| 22445/22445 [00:00<00:00, 27862.23it/s]

22445
300
<class 'list'>
In [82]:
# average Word2Vec
# compute average word2vec for each essay.
avg_w2v_title_cv = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(X_cv['project_title'].values): # for each review/sentence
    vector = np.zeros(300) # as word vectors are of zero length
    cnt_words =0; # num of words with a valid vector in the sentence/review
    for word in sentence.split(): # for each word in a review/sentence
        if word in glove_words:
            vector += model[word]
            cnt_words += 1
    if cnt_words != 0:
        vector /= cnt_words
    avg_w2v_title_cv.append(vector)

print(len(avg_w2v_title_cv))
print(len(avg_w2v_title_cv[0]))
print(type(avg_w2v_title_cv))

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<class 'list'>
In [83]:
# average Word2Vec
# compute average word2vec for each essay.
avg_w2v_title_test = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(X_test['project_title'].values): # for each review/sentence
    vector = np.zeros(300) # as word vectors are of zero length
    cnt_words =0; # num of words with a valid vector in the sentence/review
    for word in sentence.split(): # for each word in a review/sentence
        if word in glove_words:
            vector += model[word]
            cnt_words += 1
    if cnt_words != 0:
        vector /= cnt_words
    avg_w2v_title_test.append(vector)

print(len(avg_w2v_title_test))
print(len(avg_w2v_title_test[0]))
print(type(avg_w2v_title_test))

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<class 'list'>

TFIDF-W2V of Essay

In [84]:
tfidf_model = TfidfVectorizer(min_df=10,ngram_range=(1,4), max_features=5000)
tfidf_model.fit(X_train['essay'].values)
# we are converting a dictionary with word as a key, and the idf as a value
dictionary = dict(zip(tfidf_model.get_feature_names(), list(tfidf_model.idf_)))
tfidf_words = set(tfidf_model.get_feature_names())
In [85]:
# average Word2Vec
# compute average word2vec for each review.
tfidf_w2v_train_essay = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(X_train['essay'].values): # for each review/sentence
    vector = np.zeros(300) # as word vectors are of zero length
    tf_idf_weight =0; # num of words with a valid vector in the sentence/review
    for word in sentence.split(): # for each word in a review/sentence
        if (word in glove_words) and (word in tfidf_words):
            vec = model[word] # getting the vector for each word
            # here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
            tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
            vector += (vec * tf_idf) # calculating tfidf weighted w2v
            tf_idf_weight += tf_idf
    if tf_idf_weight != 0:
        vector /= tf_idf_weight
    tfidf_w2v_train_essay.append(vector)

print(len(tfidf_w2v_train_essay))
print(len(tfidf_w2v_train_essay[0]))

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In [86]:
# average Word2Vec
# compute average word2vec for each review.
tfidf_w2v_cv_essay = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(X_cv['essay'].values): # for each review/sentence
    vector = np.zeros(300) # as word vectors are of zero length
    tf_idf_weight =0; # num of words with a valid vector in the sentence/review
    for word in sentence.split(): # for each word in a review/sentence
        if (word in glove_words) and (word in tfidf_words):
            vec = model[word] # getting the vector for each word
            # here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
            tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
            vector += (vec * tf_idf) # calculating tfidf weighted w2v
            tf_idf_weight += tf_idf
    if tf_idf_weight != 0:
        vector /= tf_idf_weight
    tfidf_w2v_cv_essay.append(vector)

print(len(tfidf_w2v_cv_essay))
print(len(tfidf_w2v_cv_essay[0]))

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In [87]:
# average Word2Vec
# compute average word2vec for each review.
tfidf_w2v_test_essay = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(X_test['essay'].values): # for each review/sentence
    vector = np.zeros(300) # as word vectors are of zero length
    tf_idf_weight =0; # num of words with a valid vector in the sentence/review
    for word in sentence.split(): # for each word in a review/sentence
        if (word in glove_words) and (word in tfidf_words):
            vec = model[word] # getting the vector for each word
            # here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
            tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
            vector += (vec * tf_idf) # calculating tfidf weighted w2v
            tf_idf_weight += tf_idf
    if tf_idf_weight != 0:
        vector /= tf_idf_weight
    tfidf_w2v_test_essay.append(vector)

print(len(tfidf_w2v_test_essay))
print(len(tfidf_w2v_test_essay[0]))

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TFIDF-W2V of Title

In [88]:
tfidf_model = TfidfVectorizer(min_df=10,ngram_range=(1,4), max_features=5000)
tfidf_model.fit(X_train['project_title'].values)
# we are converting a dictionary with word as a key, and the idf as a value
dictionary = dict(zip(tfidf_model.get_feature_names(), list(tfidf_model.idf_)))
tfidf_words = set(tfidf_model.get_feature_names())
In [89]:
# average Word2Vec
# compute average word2vec for each review.
tfidf_w2v_train_title = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(X_train['project_title'].values): # for each review/sentence
    vector = np.zeros(300) # as word vectors are of zero length
    tf_idf_weight =0; # num of words with a valid vector in the sentence/review
    for word in sentence.split(): # for each word in a review/sentence
        if (word in glove_words) and (word in tfidf_words):
            vec = model[word] # getting the vector for each word
            # here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
            tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
            vector += (vec * tf_idf) # calculating tfidf weighted w2v
            tf_idf_weight += tf_idf
    if tf_idf_weight != 0:
        vector /= tf_idf_weight
    tfidf_w2v_train_title.append(vector)

print(len(tfidf_w2v_train_title))
print(len(tfidf_w2v_train_title[0]))

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In [90]:
# average Word2Vec
# compute average word2vec for each review.
tfidf_w2v_cv_title = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(X_cv['project_title'].values): # for each review/sentence
    vector = np.zeros(300) # as word vectors are of zero length
    tf_idf_weight =0; # num of words with a valid vector in the sentence/review
    for word in sentence.split(): # for each word in a review/sentence
        if (word in glove_words) and (word in tfidf_words):
            vec = model[word] # getting the vector for each word
            # here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
            tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
            vector += (vec * tf_idf) # calculating tfidf weighted w2v
            tf_idf_weight += tf_idf
    if tf_idf_weight != 0:
        vector /= tf_idf_weight
    tfidf_w2v_cv_title.append(vector)

print(len(tfidf_w2v_cv_title))
print(len(tfidf_w2v_cv_title[0]))

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In [91]:
# average Word2Vec
# compute average word2vec for each review.
tfidf_w2v_test_title = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(X_test['project_title'].values): # for each review/sentence
    vector = np.zeros(300) # as word vectors are of zero length
    tf_idf_weight =0; # num of words with a valid vector in the sentence/review
    for word in sentence.split(): # for each word in a review/sentence
        if (word in glove_words) and (word in tfidf_words):
            vec = model[word] # getting the vector for each word
            # here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
            tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
            vector += (vec * tf_idf) # calculating tfidf weighted w2v
            tf_idf_weight += tf_idf
    if tf_idf_weight != 0:
        vector /= tf_idf_weight
    tfidf_w2v_test_title.append(vector)

print(len(tfidf_w2v_test_title))
print(len(tfidf_w2v_test_title[0]))

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2.3 Make Data Model Ready: encoding eassay, and project_title

In [92]:
price_data = resource_data.groupby('id').agg({'price':'sum', 'quantity':'sum'}).reset_index()
In [93]:
price_data.head(5)
Out[93]:
id price quantity
0 p000001 459.56 7
1 p000002 515.89 21
2 p000003 298.97 4
3 p000004 1113.69 98
4 p000005 485.99 8
In [94]:
X_train=pd.merge(X_train,price_data,on='id',how='left')
X_test=pd.merge(X_test,price_data,on='id',how='left')
X_cv=pd.merge(X_cv,price_data,on='id',how='left')
In [95]:
X_train=X_train.fillna(0)
X_cv=X_cv.fillna(0)
X_test=X_test.fillna(0)

Normalizing the numerical features: Price

In [96]:
from sklearn.preprocessing import Normalizer
normalizer = Normalizer()
# normalizer.fit(X_train['price'].values)
# this will rise an error Expected 2D array, got 1D array instead: 
# array=[105.22 215.96  96.01 ... 368.98  80.53 709.67].
# Reshape your data either using 
# array.reshape(-1, 1) if your data has a single feature 
# array.reshape(1, -1)  if it contains a single sample.
normalizer.fit(X_train['price'].values.reshape(-1,1))
X_train_price_norm = normalizer.transform(X_train['price'].values.reshape(-1,1))
X_cv_price_norm = normalizer.transform(X_cv['price'].values.reshape(-1,1))
X_test_price_norm = normalizer.transform(X_test['price'].values.reshape(-1,1))
print("After vectorizations")
print(X_train_price_norm.shape, y_train.shape)
print(X_cv_price_norm.shape, y_cv.shape)
print(X_test_price_norm.shape, y_test.shape)
print("="*100)

After vectorizations
(22445, 1) (22445,)
(11055, 1) (11055,)
(16500, 1) (16500,)
====================================================================================================

Normalizing the numerical features: Number of previously posted projects

In [97]:
normalizer = Normalizer()
normalizer.fit(X_train['teacher_number_of_previously_posted_projects'].values.reshape(-1,1))
X_train_project_norm = normalizer.transform(X_train['teacher_number_of_previously_posted_projects'].values.reshape(-1,1))
X_cv_project_norm = normalizer.transform(X_cv['teacher_number_of_previously_posted_projects'].values.reshape(-1,1))
X_test_project_norm = normalizer.transform(X_test['teacher_number_of_previously_posted_projects'].values.reshape(-1,1))
print("After vectorizations")
print(X_train_project_norm.shape, y_train.shape)
print(X_cv_project_norm.shape, y_cv.shape)
print(X_test_project_norm.shape, y_test.shape)
print("="*100)

After vectorizations
(22445, 1) (22445,)
(11055, 1) (11055,)
(16500, 1) (16500,)
====================================================================================================

Normalizing the numerical features: Title word Count

In [98]:
normalizer = Normalizer()
normalizer.fit(X_train['title_word_count'].values.reshape(-1,1))
X_train_title_norm = normalizer.transform(X_train['title_word_count'].values.reshape(-1,1))
X_cv_title_norm = normalizer.transform(X_cv['title_word_count'].values.reshape(-1,1))
X_test_title_norm = normalizer.transform(X_test['title_word_count'].values.reshape(-1,1))
print("After vectorizations")
print(X_train_title_norm.shape, y_train.shape)
print(X_cv_title_norm.shape, y_cv.shape)
print(X_test_title_norm.shape, y_test.shape)
print("="*100)

After vectorizations
(22445, 1) (22445,)
(11055, 1) (11055,)
(16500, 1) (16500,)
====================================================================================================

Normalizing the numerical features: Essay word Count

In [99]:
normalizer = Normalizer()
normalizer.fit(X_train['essay_word_count'].values.reshape(-1,1))
X_train_essay_norm = normalizer.transform(X_train['essay_word_count'].values.reshape(-1,1))
X_cv_essay_norm = normalizer.transform(X_cv['essay_word_count'].values.reshape(-1,1))
X_test_essay_norm = normalizer.transform(X_test['essay_word_count'].values.reshape(-1,1))
print("After vectorizations")
print(X_train_essay_norm.shape, y_train.shape)
print(X_cv_essay_norm.shape, y_cv.shape)
print(X_test_essay_norm.shape, y_test.shape)
print("="*100)

After vectorizations
(22445, 1) (22445,)
(11055, 1) (11055,)
(16500, 1) (16500,)
====================================================================================================

Normalizing the numerical features: Essay Sentiments-Positive

In [100]:
normalizer = Normalizer()
normalizer.fit(X_train['pos'].values.reshape(-1,1))
essay_sent_pos_train = normalizer.transform(X_train['pos'].values.reshape(-1,1))
essay_sent_pos_cv = normalizer.transform(X_cv['pos'].values.reshape(-1,1))
essay_sent_pos_test = normalizer.transform(X_test['pos'].values.reshape(-1,1))
print("After vectorizations")
print(essay_sent_pos_train.shape, y_train.shape)
print(essay_sent_pos_cv.shape, y_cv.shape)
print(essay_sent_pos_test.shape, y_test.shape)
print("="*100)

After vectorizations
(22445, 1) (22445,)
(11055, 1) (11055,)
(16500, 1) (16500,)
====================================================================================================

Normalizing the numerical features: Essay Sentiments-Negative

In [101]:
normalizer = Normalizer()
normalizer.fit(X_train['neg'].values.reshape(-1,1))
essay_sent_neg_train = normalizer.transform(X_train['neg'].values.reshape(-1,1))
essay_sent_neg_cv = normalizer.transform(X_cv['neg'].values.reshape(-1,1))
essay_sent_neg_test = normalizer.transform(X_test['neg'].values.reshape(-1,1))
print("After vectorizations")
print(essay_sent_neg_train.shape, y_train.shape)
print(essay_sent_neg_cv.shape, y_cv.shape)
print(essay_sent_neg_test.shape, y_test.shape)
print("="*100)

After vectorizations
(22445, 1) (22445,)
(11055, 1) (11055,)
(16500, 1) (16500,)
====================================================================================================

Normalizing the numerical features: Essay Sentiments-Neutral

In [102]:
normalizer = Normalizer()
normalizer.fit(X_train['neu'].values.reshape(-1,1))
essay_sent_neu_train = normalizer.transform(X_train['neu'].values.reshape(-1,1))
essay_sent_neu_cv = normalizer.transform(X_cv['neu'].values.reshape(-1,1))
essay_sent_neu_test = normalizer.transform(X_test['neu'].values.reshape(-1,1))
print("After vectorizations")
print(essay_sent_neu_train.shape, y_train.shape)
print(essay_sent_neu_cv.shape, y_cv.shape)
print(essay_sent_neu_test.shape, y_test.shape)
print("="*100)

After vectorizations
(22445, 1) (22445,)
(11055, 1) (11055,)
(16500, 1) (16500,)
====================================================================================================

Normalizing the numerical features: Essay Sentiments-Compound

In [103]:
normalizer = Normalizer()
normalizer.fit(X_train['compound'].values.reshape(-1,1))
essay_sent_comp_train = normalizer.transform(X_train['compound'].values.reshape(-1,1))
essay_sent_comp_cv = normalizer.transform(X_cv['compound'].values.reshape(-1,1))
essay_sent_comp_test = normalizer.transform(X_test['compound'].values.reshape(-1,1))
print("After vectorizations")
print(essay_sent_comp_train.shape, y_train.shape)
print(essay_sent_comp_cv.shape, y_cv.shape)
print(essay_sent_comp_test.shape, y_test.shape)
print("="*100)

After vectorizations
(22445, 1) (22445,)
(11055, 1) (11055,)
(16500, 1) (16500,)
====================================================================================================

Vectorizing Categorical features

  • school_state : categorical data
  • clean_categories : categorical data
  • clean_subcategories : categorical data
  • project_grade_category : categorical data
  • teacher_prefix : categorical data

Vectorizing Categorical features: project grade category

In [104]:
from sklearn.feature_extraction.text import CountVectorizer
In [105]:
vectorizer = CountVectorizer(vocabulary=list(sorted_grade_dict.keys()), lowercase=False, binary=True)
vectorizer.fit(X_train['project_grade_category'].values) # fit has to happen only on train data

# we use the fitted CountVectorizer to convert the text to vector
X_train_grade_ohe = vectorizer.transform(X_train['project_grade_category'].values)
X_cv_grade_ohe = vectorizer.transform(X_cv['project_grade_category'].values)
X_test_grade_ohe = vectorizer.transform(X_test['project_grade_category'].values)

print("After vectorizations")
print(X_train_grade_ohe.shape, y_train.shape)
print(X_cv_grade_ohe.shape, y_cv.shape)
print(X_test_grade_ohe.shape, y_test.shape)
print(vectorizer.get_feature_names())
print("="*100)

After vectorizations
(22445, 4) (22445,)
(11055, 4) (11055,)
(16500, 4) (16500,)
['grades_9_12', 'grades_6_8', 'grades_3_5', 'grades_prek_2']
====================================================================================================

Vectorizing Categorical features: teacher prefix

In [106]:
vectorizer = CountVectorizer(vocabulary=list(sorted_teacher_dict.keys()), lowercase=False, binary=True)
vectorizer.fit(X_train['teacher_prefix'].values) # fit has to happen only on train data

# we use the fitted CountVectorizer to convert the text to vector
X_train_teacher_ohe = vectorizer.transform(X_train['teacher_prefix'].values)
X_cv_teacher_ohe = vectorizer.transform(X_cv['teacher_prefix'].values)
X_test_teacher_ohe = vectorizer.transform(X_test['teacher_prefix'].values)

print("After vectorizations")
print(X_train_teacher_ohe.shape, y_train.shape)
print(X_cv_teacher_ohe.shape, y_cv.shape)
print(X_test_teacher_ohe.shape, y_test.shape)
print(vectorizer.get_feature_names())
print("="*100)

After vectorizations
(22445, 5) (22445,)
(11055, 5) (11055,)
(16500, 5) (16500,)
['dr', 'teacher', 'mr', 'ms', 'mrs']
====================================================================================================

Vectorizing Categorical features: school state

In [107]:
vectorizer = CountVectorizer(vocabulary=list(sorted_state_dict.keys()), lowercase=False, binary=True)
vectorizer.fit(X_train['school_state'].values) # fit has to happen only on train data

# we use the fitted CountVectorizer to convert the text to vector
X_train_state_ohe = vectorizer.transform(X_train['school_state'].values)
X_cv_state_ohe = vectorizer.transform(X_cv['school_state'].values)
X_test_state_ohe = vectorizer.transform(X_test['school_state'].values)

print("After vectorizations")
print(X_train_state_ohe.shape, y_train.shape)
print(X_cv_state_ohe.shape, y_cv.shape)
print(X_test_state_ohe.shape, y_test.shape)
print(vectorizer.get_feature_names())
print("="*100)

After vectorizations
(22445, 51) (22445,)
(11055, 51) (11055,)
(16500, 51) (16500,)
['WY', 'VT', 'ND', 'MT', 'RI', 'NE', 'SD', 'NH', 'AK', 'DE', 'WV', 'HI', 'ME', 'DC', 'NM', 'KS', 'IA', 'ID', 'AR', 'CO', 'OR', 'MN', 'NV', 'KY', 'MS', 'MD', 'TN', 'WI', 'AL', 'CT', 'UT', 'VA', 'AZ', 'NJ', 'OK', 'LA', 'WA', 'MA', 'OH', 'IN', 'MO', 'PA', 'MI', 'SC', 'GA', 'IL', 'NC', 'FL', 'TX', 'NY', 'CA']
====================================================================================================

Vectorizing Categorical features: clean categories

In [108]:
vectorizer = CountVectorizer(vocabulary=list(sorted_cat_dict.keys()), lowercase=False, binary=True)
vectorizer.fit(X_train['clean_categories'].values) # fit has to happen only on train data

# we use the fitted CountVectorizer to convert the text to vector
X_train_cat_ohe = vectorizer.transform(X_train['clean_categories'].values)
X_cv_cat_ohe = vectorizer.transform(X_cv['clean_categories'].values)
X_test_cat_ohe = vectorizer.transform(X_test['clean_categories'].values)

print("After vectorizations")
print(X_train_cat_ohe.shape, y_train.shape)
print(X_cv_cat_ohe.shape, y_cv.shape)
print(X_test_cat_ohe.shape, y_test.shape)
print(vectorizer.get_feature_names())
print("="*100)

After vectorizations
(22445, 9) (22445,)
(11055, 9) (11055,)
(16500, 9) (16500,)
['Warmth', 'Care_Hunger', 'History_Civics', 'Music_Arts', 'AppliedLearning', 'SpecialNeeds', 'Health_Sports', 'Math_Science', 'Literacy_Language']
====================================================================================================

Vectorizing Categorical features: clean subcategories

In [109]:
vectorizer = CountVectorizer(vocabulary=list(sorted_sub_cat_dict.keys()), lowercase=False, binary=True)
vectorizer.fit(X_train['school_state'].values) # fit has to happen only on train data

# we use the fitted CountVectorizer to convert the text to vector
X_train_sub_ohe = vectorizer.transform(X_train['clean_subcategories'].values)
X_cv_sub_ohe = vectorizer.transform(X_cv['clean_subcategories'].values)
X_test_sub_ohe = vectorizer.transform(X_test['clean_subcategories'].values)

print("After vectorizations")
print(X_train_sub_ohe.shape, y_train.shape)
print(X_cv_sub_ohe.shape, y_cv.shape)
print(X_test_sub_ohe.shape, y_test.shape)
print(vectorizer.get_feature_names())
print("="*100)

After vectorizations
(22445, 30) (22445,)
(11055, 30) (11055,)
(16500, 30) (16500,)
['Economics', 'CommunityService', 'FinancialLiteracy', 'ParentInvolvement', 'Extracurricular', 'Civics_Government', 'ForeignLanguages', 'NutritionEducation', 'Warmth', 'Care_Hunger', 'SocialSciences', 'CharacterEducation', 'PerformingArts', 'TeamSports', 'Other', 'College_CareerPrep', 'Music', 'History_Geography', 'EarlyDevelopment', 'Health_LifeScience', 'ESL', 'Gym_Fitness', 'EnvironmentalScience', 'VisualArts', 'Health_Wellness', 'AppliedSciences', 'SpecialNeeds', 'Literature_Writing', 'Mathematics', 'Literacy']
====================================================================================================

2.4 Appling Logistic Regression on different kind of featurization as mentioned in the instructions


Apply Logistic Regression on different kind of featurization as mentioned in the instructions
For Every model that you work on make sure you do the step 2 and step 3 of instrucations

Applying Logistic Regression on BOW, SET 1

Creating Data Matrix

In [110]:
# Please write all the code with proper documentation

# merge two sparse matrices: https://stackoverflow.com/a/19710648/4084039
from scipy.sparse import hstack
X_tr = hstack((X_train_essay_bow,X_train_title_bow, X_train_state_ohe, X_train_teacher_ohe, X_train_grade_ohe,X_train_cat_ohe,X_train_sub_ohe, X_train_price_norm,X_train_project_norm)).tocsr()
X_cr = hstack((X_cv_essay_bow,X_cv_title_bow, X_cv_state_ohe, X_cv_teacher_ohe, X_cv_grade_ohe,X_cv_cat_ohe,X_cv_sub_ohe, X_cv_price_norm,X_cv_project_norm)).tocsr()
X_te = hstack((X_test_essay_bow,X_test_title_bow, X_test_state_ohe, X_test_teacher_ohe, X_test_grade_ohe,X_test_cat_ohe,X_test_sub_ohe, X_test_price_norm,X_test_project_norm)).tocsr()

print("Final Data matrix")
print(X_tr.shape, y_train.shape)
print(X_cr.shape, y_cv.shape)
print(X_te.shape, y_test.shape)
print("="*100)

Final Data matrix
(22445, 7111) (22445,)
(11055, 7111) (11055,)
(16500, 7111) (16500,)
====================================================================================================

Hyperparameter Tuning: Simple for loop (if you are having memory limitations use this)

In [111]:
def batch_predict(clf, data):
    # roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
    # not the predicted outputs

    y_data_pred = []
    tr_loop = data.shape[0] - data.shape[0]%1000
    # consider you X_tr shape is 49041, then your cr_loop will be 49041 - 49041%1000 = 49000
    # in this for loop we will iterate unti the last 1000 multiplier
    for i in range(0, tr_loop, 1000):
        y_data_pred.extend(clf.predict_proba(data[i:i+1000])[:,1])
    # we will be predicting for the last data points
    y_data_pred.extend(clf.predict_proba(data[tr_loop:])[:,1])
    
    return y_data_pred
In [112]:
import matplotlib.pyplot as plt
from sklearn.metrics import roc_auc_score
from sklearn.linear_model import LogisticRegression
"""
y_true : array, shape = [n_samples] or [n_samples, n_classes]
True binary labels or binary label indicators.

y_score : array, shape = [n_samples] or [n_samples, n_classes]
Target scores, can either be probability estimates of the positive class, confidence values, or non-thresholded measure of
decisions (as returned by “decision_function” on some classifiers). 
For binary y_true, y_score is supposed to be the score of the class with greater label.

"""

train_auc = []
cv_auc = []
log_alphas=[]

parameters = {'C':[0.0001,0.0005,0.001,0.005,0.01,0.05,0.1,0.5,1,2.5,5]}

for i in tqdm(parameters['C']):
    neigh = LogisticRegression(C=i)
    neigh.fit(X_tr, y_train)

    y_train_pred = batch_predict(neigh, X_tr)    
    y_cv_pred = batch_predict(neigh, X_cr)

    # roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
    # not the predicted outputs        
    train_auc.append(roc_auc_score(y_train,y_train_pred))
    cv_auc.append(roc_auc_score(y_cv, y_cv_pred))

100%|██████████████████████████████████████████████████████████████████████████████████| 11/11 [01:05<00:00, 13.16s/it]
In [113]:
import math 
for a in tqdm(parameters['C']):
    b = math.log10(a)
    log_alphas.append(b)
print(log_alphas)

100%|███████████████████████████████████████████████████████████████████████████████| 11/11 [00:00<00:00, 10927.84it/s]

[-4.0, -3.3010299956639813, -3.0, -2.3010299956639813, -2.0, -1.3010299956639813, -1.0, -0.3010299956639812, 0.0, 0.3979400086720376, 0.6989700043360189]
In [114]:
plt.figure(figsize=(20,15))
plt.plot(log_alphas, train_auc, label='Train AUC')
plt.plot(log_alphas, cv_auc, label='CV AUC')

plt.scatter(log_alphas, train_auc, label='Train AUC points')
plt.scatter(log_alphas, cv_auc, label='CV AUC points')

plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("AUC")
plt.title("ERROR PLOTS")
plt.grid()
plt.show()
In [115]:
best_k=0.005

Train The Model

In [116]:
from sklearn.metrics import roc_curve, auc

neigh = LogisticRegression(C=best_k)
neigh.fit(X_tr, y_train)
# roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
# not the predicted outputs

y_train_pred = batch_predict(neigh, X_tr)    
y_test_pred = batch_predict(neigh, X_te)

train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)
test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)

x=[0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]
In [117]:
# from sklearn.metrics import roc_curve, auc

# neigh = LogisticRegression(C=best_k)
# neigh.fit(X_tr, y_train)
# # roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
# # not the predicted outputs

# y_train_pred = neigh.predict_proba(X_tr)    
# y_test_pred = neigh.predict_proba(X_te)

# train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred[:,1])
# test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred[:,1])

# x=[0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]
In [118]:
plt.plot(train_fpr, train_tpr, label="train AUC ="+str(auc(train_fpr, train_tpr)))
plt.plot(test_fpr, test_tpr, label="test AUC ="+str(auc(test_fpr, test_tpr)))
plt.plot(x,x)
plt.legend()
plt.xlabel("K: hyperparameter")
plt.ylabel("AUC")
plt.title("ERROR PLOTS")
plt.grid()
plt.show()

Confusion Matrix

In [119]:
# we are writing our own function for predict, with defined thresould
# we will pick a threshold that will give the least fpr
def predict(proba, threshould, fpr, tpr):
    
    t = threshould[np.argmax(fpr*(1-tpr))]
    
    # (tpr*(1-fpr)) will be maximum if your fpr is very low and tpr is very high
    
    print("the maximum value of tpr*(1-fpr)", max(tpr*(1-fpr)), "for threshold", np.round(t,3))
    predictions = []
    for i in proba:
        if i>=t:
            predictions.append(1)
        else:
            predictions.append(0)
    return predictions
In [120]:
print("Train confusion matrix")
conf_matr_df_train_2=pd.DataFrame(confusion_matrix(y_train,predict(y_train_pred,tr_thresholds,train_fpr,train_fpr)),range(2),range(2))
sns.set(font_scale=1)#for label size
sns.heatmap(conf_matr_df_train_2,annot=True,annot_kws={"size":30},fmt='g')

Train confusion matrix
the maximum value of tpr*(1-fpr) 0.24999997805212623 for threshold 0.77
Out[120]:
<matplotlib.axes._subplots.AxesSubplot at 0x1881e95f898>
In [121]:
print("Test confusion matrix")
conf_matr_df_train_2=pd.DataFrame(confusion_matrix(y_test,predict(y_test_pred,tr_thresholds,test_fpr,test_fpr)),range(2),range(2))
sns.set(font_scale=1)#for label size
sns.heatmap(conf_matr_df_train_2,annot=True,annot_kws={"size":30},fmt='g')

Test confusion matrix
the maximum value of tpr*(1-fpr) 0.2499999593849979 for threshold 0.804
Out[121]:
<matplotlib.axes._subplots.AxesSubplot at 0x188129c0828>

2.4.2 Applying Logistic Regression on TFIDF, SET 2

Creating Data Matrix

In [122]:
# Please write all the code with proper documentation


# merge two sparse matrices: https://stackoverflow.com/a/19710648/4084039
from scipy.sparse import hstack
X_tr = hstack((X_train_essay_tfidf,X_train_title_tfidf, X_train_state_ohe, X_train_teacher_ohe, X_train_grade_ohe,X_train_cat_ohe,X_train_sub_ohe, X_train_price_norm,X_train_project_norm)).tocsr()
X_cr = hstack((X_cv_essay_tfidf,X_cv_title_tfidf, X_cv_state_ohe, X_cv_teacher_ohe, X_cv_grade_ohe,X_cv_cat_ohe,X_cv_sub_ohe, X_cv_price_norm,X_cv_project_norm)).tocsr()
X_te = hstack((X_test_essay_tfidf,X_test_title_tfidf, X_test_state_ohe, X_test_teacher_ohe, X_test_grade_ohe,X_test_cat_ohe,X_test_sub_ohe, X_test_price_norm,X_test_project_norm)).tocsr()

print("Final Data matrix")
print(X_tr.shape, y_train.shape)
print(X_cr.shape, y_cv.shape)
print(X_te.shape, y_test.shape)
print("="*100)

Final Data matrix
(22445, 7111) (22445,)
(11055, 7111) (11055,)
(16500, 7111) (16500,)
====================================================================================================

Hyperparameter Tuning: Simple for loop (if you are having memory limitations use this)

In [123]:
def batch_predict(clf, data):
    # roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
    # not the predicted outputs

    y_data_pred = []
    tr_loop = data.shape[0] - data.shape[0]%1000
    # consider you X_tr shape is 49041, then your cr_loop will be 49041 - 49041%1000 = 49000
    # in this for loop we will iterate unti the last 1000 multiplier
    for i in range(0, tr_loop, 1000):
        y_data_pred.extend(clf.predict_proba(data[i:i+1000])[:,1])
    # we will be predicting for the last data points
    y_data_pred.extend(clf.predict_proba(data[tr_loop:])[:,1])
    
    return y_data_pred
In [124]:
import matplotlib.pyplot as plt
from sklearn.metrics import roc_auc_score
from sklearn.linear_model import LogisticRegression
"""
y_true : array, shape = [n_samples] or [n_samples, n_classes]
True binary labels or binary label indicators.

y_score : array, shape = [n_samples] or [n_samples, n_classes]
Target scores, can either be probability estimates of the positive class, confidence values, or non-thresholded measure of
decisions (as returned by “decision_function” on some classifiers). 
For binary y_true, y_score is supposed to be the score of the class with greater label.

"""

train_auc = []
cv_auc = []
log_alphas=[]
parameters = {'C':[0.0001,0.0005,0.001,0.005,0.01,0.05,0.1,0.5,1,2.5,5,7.5,10]}

for i in tqdm(parameters['C']):
    neigh = LogisticRegression(C=i)
    neigh.fit(X_tr, y_train)

    y_train_pred = batch_predict(neigh, X_tr)    
    y_cv_pred = batch_predict(neigh, X_cr)

    # roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
    # not the predicted outputs        
    train_auc.append(roc_auc_score(y_train,y_train_pred))
    cv_auc.append(roc_auc_score(y_cv, y_cv_pred))

100%|██████████████████████████████████████████████████████████████████████████████████| 13/13 [00:43<00:00,  6.76s/it]
In [125]:
for a in tqdm(parameters['C']):
    b = math.log10(a)
    log_alphas.append(b)
print(log_alphas)

100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:00<?, ?it/s]

[-4.0, -3.3010299956639813, -3.0, -2.3010299956639813, -2.0, -1.3010299956639813, -1.0, -0.3010299956639812, 0.0, 0.3979400086720376, 0.6989700043360189, 0.8750612633917001, 1.0]
In [126]:
plt.figure(figsize=(20,15))
plt.plot(log_alphas, train_auc, label='Train AUC')
plt.plot(log_alphas, cv_auc, label='CV AUC')

plt.scatter(log_alphas, train_auc, label='Train AUC points')
plt.scatter(log_alphas, cv_auc, label='CV AUC points')

plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("AUC")
plt.title("ERROR PLOTS")
plt.grid()
plt.show()
In [127]:
best_k=0.30

Train The Model

In [128]:
from sklearn.metrics import roc_curve, auc

neigh = LogisticRegression(C=best_k)
neigh.fit(X_tr, y_train)
# roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
# not the predicted outputs

y_train_pred = batch_predict(neigh, X_tr)    
y_test_pred = batch_predict(neigh, X_te)

train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)
test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)

x=[0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]
In [129]:
plt.plot(train_fpr, train_tpr, label="train AUC ="+str(auc(train_fpr, train_tpr)))
plt.plot(test_fpr, test_tpr, label="test AUC ="+str(auc(test_fpr, test_tpr)))
plt.plot(x,x)
plt.legend()
plt.xlabel("K: hyperparameter")
plt.ylabel("AUC")
plt.title("ERROR PLOTS")
plt.grid()
plt.show()

Confusion Matrix

In [130]:
# we are writing our own function for predict, with defined thresould
# we will pick a threshold that will give the least fpr
def predict(proba, threshould, fpr, tpr):
    
    t = threshould[np.argmax(fpr*(1-tpr))]
    
    # (tpr*(1-fpr)) will be maximum if your fpr is very low and tpr is very high
    
    print("the maximum value of tpr*(1-fpr)", max(tpr*(1-fpr)), "for threshold", np.round(t,3))
    predictions = []
    for i in proba:
        if i>=t:
            predictions.append(1)
        else:
            predictions.append(0)
    return predictions
In [131]:
print("Train confusion matrix")
conf_matr_df_train_2=pd.DataFrame(confusion_matrix(y_train,predict(y_train_pred,tr_thresholds,train_fpr,train_fpr)),range(2),range(2))
sns.set(font_scale=1)#for label size
sns.heatmap(conf_matr_df_train_2,annot=True,annot_kws={"size":30},fmt='g')

Train confusion matrix
the maximum value of tpr*(1-fpr) 0.24999997805212623 for threshold 0.771
Out[131]:
<matplotlib.axes._subplots.AxesSubplot at 0x188116c4a20>
In [132]:
print("Test confusion matrix")
conf_matr_df_train_2=pd.DataFrame(confusion_matrix(y_test,predict(y_test_pred,tr_thresholds,test_fpr,test_fpr)),range(2),range(2))
sns.set(font_scale=1)#for label size
sns.heatmap(conf_matr_df_train_2,annot=True,annot_kws={"size":30},fmt='g')

Test confusion matrix
the maximum value of tpr*(1-fpr) 0.2499999593849979 for threshold 0.805
Out[132]:
<matplotlib.axes._subplots.AxesSubplot at 0x18813a8d278>

2.4.3 Applying Logistic Regression on AVG W2V, SET 3

Creating Data Matrix

In [133]:
# Please write all the code with proper documentation

# merge two sparse matrices: https://stackoverflow.com/a/19710648/4084039
from scipy.sparse import hstack
X_tr = hstack((avg_w2v_essay_train,avg_w2v_title_train, X_train_state_ohe, X_train_teacher_ohe, X_train_grade_ohe,X_train_cat_ohe,X_train_sub_ohe, X_train_price_norm,X_train_project_norm)).tocsr()
X_cr = hstack((avg_w2v_essay_cv,avg_w2v_title_cv, X_cv_state_ohe, X_cv_teacher_ohe, X_cv_grade_ohe,X_cv_cat_ohe,X_cv_sub_ohe, X_cv_price_norm,X_cv_project_norm)).tocsr()
X_te = hstack((avg_w2v_essay_test,avg_w2v_title_test, X_test_state_ohe, X_test_teacher_ohe, X_test_grade_ohe,X_test_cat_ohe,X_test_sub_ohe, X_test_price_norm,X_test_project_norm)).tocsr()

print("Final Data matrix")
print(X_tr.shape, y_train.shape)
print(X_cr.shape, y_cv.shape)
print(X_te.shape, y_test.shape)
print("="*100)

Final Data matrix
(22445, 701) (22445,)
(11055, 701) (11055,)
(16500, 701) (16500,)
====================================================================================================

Hyperparameter Tuning: Simple for loop (if you are having memory limitations use this)

In [134]:
def batch_predict(clf, data):
    # roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
    # not the predicted outputs

    y_data_pred = []
    tr_loop = data.shape[0] - data.shape[0]%1000
    # consider you X_tr shape is 49041, then your cr_loop will be 49041 - 49041%1000 = 49000
    # in this for loop we will iterate unti the last 1000 multiplier
    for i in range(0, tr_loop, 1000):
        y_data_pred.extend(clf.predict_proba(data[i:i+1000])[:,1])
    # we will be predicting for the last data points
    y_data_pred.extend(clf.predict_proba(data[tr_loop:])[:,1])
    
    return y_data_pred
In [135]:
import matplotlib.pyplot as plt
from sklearn.metrics import roc_auc_score
from sklearn.linear_model import LogisticRegression
"""
y_true : array, shape = [n_samples] or [n_samples, n_classes]
True binary labels or binary label indicators.

y_score : array, shape = [n_samples] or [n_samples, n_classes]
Target scores, can either be probability estimates of the positive class, confidence values, or non-thresholded measure of
decisions (as returned by “decision_function” on some classifiers). 
For binary y_true, y_score is supposed to be the score of the class with greater label.

"""

train_auc = []
cv_auc = []
log_alphas=[]
parameters = {'C':[0.01,0.05,0.1,0.5,1,5,10,50,100,500,1000,5000,10000,50000]}

for i in tqdm(parameters['C']):
    neigh = LogisticRegression(C=i)
    neigh.fit(X_tr, y_train)

    y_train_pred = batch_predict(neigh, X_tr)    
    y_cv_pred = batch_predict(neigh, X_cr)

    # roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
    # not the predicted outputs        
    train_auc.append(roc_auc_score(y_train,y_train_pred))
    cv_auc.append(roc_auc_score(y_cv, y_cv_pred))

100%|██████████████████████████████████████████████████████████████████████████████████| 14/14 [08:00<00:00, 53.23s/it]
In [136]:
for a in tqdm(parameters['C']):
    b = math.log10(a)
    log_alphas.append(b)
print(log_alphas)

100%|███████████████████████████████████████████████████████████████████████████████| 14/14 [00:00<00:00, 14051.27it/s]

[-2.0, -1.3010299956639813, -1.0, -0.3010299956639812, 0.0, 0.6989700043360189, 1.0, 1.6989700043360187, 2.0, 2.6989700043360187, 3.0, 3.6989700043360187, 4.0, 4.698970004336019]
In [137]:
plt.figure(figsize=(20,15))
plt.plot(log_alphas, train_auc, label='Train AUC')
plt.plot(log_alphas, cv_auc, label='CV AUC')

plt.scatter(log_alphas, train_auc, label='Train AUC points')
plt.scatter(log_alphas, cv_auc, label='CV AUC points')

plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("AUC")
plt.title("ERROR PLOTS")
plt.grid()
plt.show()
In [138]:
best_k=1

Train The Model

In [139]:
from sklearn.metrics import roc_curve, auc

neigh = LogisticRegression(C=best_k)
neigh.fit(X_tr, y_train)
# roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
# not the predicted outputs

y_train_pred = batch_predict(neigh, X_tr)    
y_test_pred = batch_predict(neigh, X_te)

train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)
test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)
In [140]:
x=[0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]
plt.plot(train_fpr, train_tpr, label="train AUC ="+str(auc(train_fpr, train_tpr)))
plt.plot(test_fpr, test_tpr, label="train AUC ="+str(auc(test_fpr, test_tpr)))
plt.plot(x,x)
plt.legend()
plt.xlabel("K: hyperparameter")
plt.ylabel("AUC")
plt.title("ERROR PLOTS")
plt.grid()
plt.show()

Confusion Matrix

In [141]:
# we are writing our own function for predict, with defined thresould
# we will pick a threshold that will give the least fpr
def predict(proba, threshould, fpr, tpr):
    
    t = threshould[np.argmax(fpr*(1-tpr))]
    
    # (tpr*(1-fpr)) will be maximum if your fpr is very low and tpr is very high
    
    print("the maximum value of tpr*(1-fpr)", max(tpr*(1-fpr)), "for threshold", np.round(t,3))
    predictions = []
    for i in proba:
        if i>=t:
            predictions.append(1)
        else:
            predictions.append(0)
    return predictions
In [142]:
print("Train confusion matrix")
conf_matr_df_train_2=pd.DataFrame(confusion_matrix(y_train,predict(y_train_pred,tr_thresholds,train_fpr,train_fpr)),range(2),range(2))
sns.set(font_scale=1)#for label size
sns.heatmap(conf_matr_df_train_2,annot=True,annot_kws={"size":30},fmt='g')

Train confusion matrix
the maximum value of tpr*(1-fpr) 0.2499999780521262 for threshold 0.791
Out[142]:
<matplotlib.axes._subplots.AxesSubplot at 0x1881e38ea58>
In [143]:
print("Test confusion matrix")
conf_matr_df_train_2=pd.DataFrame(confusion_matrix(y_test,predict(y_test_pred,tr_thresholds,test_fpr,test_fpr)),range(2),range(2))
sns.set(font_scale=1)#for label size
sns.heatmap(conf_matr_df_train_2,annot=True,annot_kws={"size":30},fmt='g')

Test confusion matrix
the maximum value of tpr*(1-fpr) 0.2499999593849979 for threshold 0.833
Out[143]:
<matplotlib.axes._subplots.AxesSubplot at 0x1881b578fd0>

2.4.4 Applying Logistic Regression on TFIDF W2V, SET 4

Creating Data Matrix

In [144]:
# Please write all the code with proper documentation

# merge two sparse matrices: https://stackoverflow.com/a/19710648/4084039
from scipy.sparse import hstack
X_tr = hstack((tfidf_w2v_train_essay,tfidf_w2v_train_title, X_train_state_ohe, X_train_teacher_ohe, X_train_grade_ohe,X_train_cat_ohe,X_train_sub_ohe, X_train_price_norm,X_train_project_norm)).tocsr()
X_cr = hstack((tfidf_w2v_cv_essay,tfidf_w2v_cv_title, X_cv_state_ohe, X_cv_teacher_ohe, X_cv_grade_ohe,X_cv_cat_ohe,X_cv_sub_ohe, X_cv_price_norm,X_cv_project_norm)).tocsr()
X_te = hstack((tfidf_w2v_test_essay,tfidf_w2v_test_title, X_test_state_ohe, X_test_teacher_ohe, X_test_grade_ohe,X_test_cat_ohe,X_test_sub_ohe, X_test_price_norm,X_test_project_norm)).tocsr()

print("Final Data matrix")
print(X_tr.shape, y_train.shape)
print(X_cr.shape, y_cv.shape)
print(X_te.shape, y_test.shape)
print("="*100)

Final Data matrix
(22445, 701) (22445,)
(11055, 701) (11055,)
(16500, 701) (16500,)
====================================================================================================

Hyperparameter Tuning: Simple for loop (if you are having memory limitations use this)

In [145]:
def batch_predict(clf, data):
    # roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
    # not the predicted outputs

    y_data_pred = []
    tr_loop = data.shape[0] - data.shape[0]%1000
    # consider you X_tr shape is 49041, then your cr_loop will be 49041 - 49041%1000 = 49000
    # in this for loop we will iterate unti the last 1000 multiplier
    for i in range(0, tr_loop, 1000):
        y_data_pred.extend(clf.predict_proba(data[i:i+1000])[:,1])
    # we will be predicting for the last data points
    y_data_pred.extend(clf.predict_proba(data[tr_loop:])[:,1])
    
    return y_data_pred
In [146]:
import matplotlib.pyplot as plt
from sklearn.metrics import roc_auc_score
from sklearn.linear_model import LogisticRegression
"""
y_true : array, shape = [n_samples] or [n_samples, n_classes]
True binary labels or binary label indicators.

y_score : array, shape = [n_samples] or [n_samples, n_classes]
Target scores, can either be probability estimates of the positive class, confidence values, or non-thresholded measure of
decisions (as returned by “decision_function” on some classifiers). 
For binary y_true, y_score is supposed to be the score of the class with greater label.

"""

train_auc = []
cv_auc = []
log_alphas=[]
parameters = {'C':[0.0001,0.0005,0.001,0.005,0.01,0.05,0.1,0.5,1,2.5,5,10,50,100,500]}

for i in tqdm(parameters['C']):
    neigh = LogisticRegression(C=i)
    neigh.fit(X_tr, y_train)

    y_train_pred = batch_predict(neigh, X_tr)    
    y_cv_pred = batch_predict(neigh, X_cr)

    # roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
    # not the predicted outputs        
    train_auc.append(roc_auc_score(y_train,y_train_pred))
    cv_auc.append(roc_auc_score(y_cv, y_cv_pred))

100%|██████████████████████████████████████████████████████████████████████████████████| 15/15 [04:57<00:00, 41.63s/it]
In [147]:
for a in tqdm(parameters['C']):
    b = math.log10(a)
    log_alphas.append(b)
print(log_alphas)

100%|██████████████████████████████████████████████████████████████████████████████████████████| 15/15 [00:00<?, ?it/s]

[-4.0, -3.3010299956639813, -3.0, -2.3010299956639813, -2.0, -1.3010299956639813, -1.0, -0.3010299956639812, 0.0, 0.3979400086720376, 0.6989700043360189, 1.0, 1.6989700043360187, 2.0, 2.6989700043360187]
In [148]:
plt.figure(figsize=(20,15))
plt.plot(log_alphas, train_auc, label='Train AUC')
plt.plot(log_alphas, cv_auc, label='CV AUC')

plt.scatter(log_alphas, train_auc, label='Train AUC points')
plt.scatter(log_alphas, cv_auc, label='CV AUC points')

plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("AUC")
plt.title("ERROR PLOTS")
plt.grid()
plt.show()
In [149]:
best_k=0.1

Train The Model

In [150]:
from sklearn.metrics import roc_curve, auc

neigh = LogisticRegression(C=best_k)
neigh.fit(X_tr, y_train)
# roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
# not the predicted outputs

y_train_pred = batch_predict(neigh, X_tr)    
y_test_pred = batch_predict(neigh, X_te)

train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)
test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)
In [151]:
x=[0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]
plt.plot(train_fpr, train_tpr, label="train AUC ="+str(auc(train_fpr, train_tpr)))
plt.plot(test_fpr, test_tpr, label="train AUC ="+str(auc(test_fpr, test_tpr)))
plt.plot(x,x)
plt.legend()
plt.xlabel("K: hyperparameter")
plt.ylabel("AUC")
plt.title("ERROR PLOTS")
plt.grid()
plt.show()

Confusion Matrix

In [152]:
# we are writing our own function for predict, with defined thresould
# we will pick a threshold that will give the least fpr
def predict(proba, threshould, fpr, tpr):
    
    t = threshould[np.argmax(fpr*(1-tpr))]
    
    # (tpr*(1-fpr)) will be maximum if your fpr is very low and tpr is very high
    
    print("the maximum value of tpr*(1-fpr)", max(tpr*(1-fpr)), "for threshold", np.round(t,3))
    predictions = []
    for i in proba:
        if i>=t:
            predictions.append(1)
        else:
            predictions.append(0)
    return predictions
In [153]:
print("Train confusion matrix")
conf_matr_df_train_2=pd.DataFrame(confusion_matrix(y_train,predict(y_train_pred,tr_thresholds,train_fpr,train_fpr)),range(2),range(2))
sns.set(font_scale=1)#for label size
sns.heatmap(conf_matr_df_train_2,annot=True,annot_kws={"size":30},fmt='g')

Train confusion matrix
the maximum value of tpr*(1-fpr) 0.24999997805212623 for threshold 0.815
Out[153]:
<matplotlib.axes._subplots.AxesSubplot at 0x1881f86cc18>
In [154]:
print("Test confusion matrix")
conf_matr_df_train_2=pd.DataFrame(confusion_matrix(y_test,predict(y_test_pred,tr_thresholds,test_fpr,test_fpr)),range(2),range(2))
sns.set(font_scale=1)#for label size
sns.heatmap(conf_matr_df_train_2,annot=True,annot_kws={"size":30},fmt='g')

Test confusion matrix
the maximum value of tpr*(1-fpr) 0.2499999593849979 for threshold 0.839
Out[154]:
<matplotlib.axes._subplots.AxesSubplot at 0x1881e38ef60>

2.5 Logistic Regression with added Features, SET 5

Creating Data Matrix

In [155]:
X_tr = hstack((X_train_state_ohe, X_train_teacher_ohe, X_train_grade_ohe,X_train_cat_ohe,X_train_sub_ohe, X_train_price_norm,X_train_project_norm,X_train_title_norm,X_train_essay_norm,essay_sent_pos_train,essay_sent_neg_train,essay_sent_neu_train,essay_sent_comp_train)).tocsr()
X_cr = hstack((X_cv_state_ohe, X_cv_teacher_ohe, X_cv_grade_ohe,X_cv_cat_ohe,X_cv_sub_ohe, X_cv_price_norm,X_cv_project_norm,X_cv_title_norm,X_cv_essay_norm,essay_sent_pos_cv,essay_sent_neg_cv,essay_sent_neu_cv,essay_sent_comp_cv)).tocsr()
X_te = hstack((X_test_state_ohe, X_test_teacher_ohe, X_test_grade_ohe,X_test_cat_ohe,X_test_sub_ohe, X_test_price_norm,X_test_project_norm,X_test_title_norm,X_test_essay_norm,essay_sent_pos_test,essay_sent_neg_test,essay_sent_neu_test,essay_sent_comp_test)).tocsr()

print("Final Data matrix")
print(X_tr.shape, y_train.shape)
print(X_cr.shape, y_cv.shape)
print(X_te.shape, y_test.shape)
print("="*100)

Final Data matrix
(22445, 107) (22445,)
(11055, 107) (11055,)
(16500, 107) (16500,)
====================================================================================================

Hyperparameter Tuning: Simple for loop (if you are having memory limitations use this)

In [156]:
def batch_predict(clf, data):
    # roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
    # not the predicted outputs

    y_data_pred = []
    tr_loop = data.shape[0] - data.shape[0]%1000
    # consider you X_tr shape is 49041, then your cr_loop will be 49041 - 49041%1000 = 49000
    # in this for loop we will iterate unti the last 1000 multiplier
    for i in range(0, tr_loop, 1000):
        y_data_pred.extend(clf.predict_proba(data[i:i+1000])[:,1])
    # we will be predicting for the last data points
    y_data_pred.extend(clf.predict_proba(data[tr_loop:])[:,1])
    
    return y_data_pred
In [157]:
import matplotlib.pyplot as plt
from sklearn.metrics import roc_auc_score
from sklearn.linear_model import LogisticRegression
"""
y_true : array, shape = [n_samples] or [n_samples, n_classes]
True binary labels or binary label indicators.

y_score : array, shape = [n_samples] or [n_samples, n_classes]
Target scores, can either be probability estimates of the positive class, confidence values, or non-thresholded measure of
decisions (as returned by “decision_function” on some classifiers). 
For binary y_true, y_score is supposed to be the score of the class with greater label.

"""

train_auc = []
cv_auc = []
log_alphas=[]

parameters = {'C':[0.0001,0.0005,0.001,0.005,0.01,0.05,0.1,0.5,1,2.5,5]}

for i in tqdm(parameters['C']):
    neigh = LogisticRegression(C=i)
    neigh.fit(X_tr, y_train)

    y_train_pred = batch_predict(neigh, X_tr)    
    y_cv_pred = batch_predict(neigh, X_cr)

    # roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
    # not the predicted outputs        
    train_auc.append(roc_auc_score(y_train,y_train_pred))
    cv_auc.append(roc_auc_score(y_cv, y_cv_pred))

100%|██████████████████████████████████████████████████████████████████████████████████| 11/11 [00:02<00:00,  2.75it/s]
In [158]:
import math 
for a in tqdm(parameters['C']):
    b = math.log10(a)
    log_alphas.append(b)
print(log_alphas)

100%|██████████████████████████████████████████████████████████████████████████████████████████| 11/11 [00:00<?, ?it/s]

[-4.0, -3.3010299956639813, -3.0, -2.3010299956639813, -2.0, -1.3010299956639813, -1.0, -0.3010299956639812, 0.0, 0.3979400086720376, 0.6989700043360189]
In [159]:
plt.figure(figsize=(20,15))
plt.plot(log_alphas, train_auc, label='Train AUC')
plt.plot(log_alphas, cv_auc, label='CV AUC')

plt.scatter(log_alphas, train_auc, label='Train AUC points')
plt.scatter(log_alphas, cv_auc, label='CV AUC points')

plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("AUC")
plt.title("ERROR PLOTS")
plt.grid()
plt.show()
In [160]:
best_k=0.01

Train The Model

In [161]:
from sklearn.metrics import roc_curve, auc

neigh = LogisticRegression(C=best_k)
neigh.fit(X_tr, y_train)
# roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
# not the predicted outputs

y_train_pred = batch_predict(neigh, X_tr)    
y_test_pred = batch_predict(neigh, X_te)

train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)
test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)
In [162]:
x=[0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]
plt.plot(train_fpr, train_tpr, label="train AUC ="+str(auc(train_fpr, train_tpr)))
plt.plot(test_fpr, test_tpr, label="train AUC ="+str(auc(test_fpr, test_tpr)))
plt.plot(x,x)
plt.legend()
plt.xlabel("K: hyperparameter")
plt.ylabel("AUC")
plt.title("ERROR PLOTS")
plt.grid()
plt.show()

Confusion Matrix

In [163]:
# we are writing our own function for predict, with defined thresould
# we will pick a threshold that will give the least fpr
def predict(proba, threshould, fpr, tpr):
    
    t = threshould[np.argmax(fpr*(1-tpr))]
    
    # (tpr*(1-fpr)) will be maximum if your fpr is very low and tpr is very high
    
    print("the maximum value of tpr*(1-fpr)", max(tpr*(1-fpr)), "for threshold", np.round(t,3))
    predictions = []
    for i in proba:
        if i>=t:
            predictions.append(1)
        else:
            predictions.append(0)
    return predictions
In [164]:
print("Train confusion matrix")
conf_matr_df_train_2=pd.DataFrame(confusion_matrix(y_train,predict(y_train_pred,tr_thresholds,train_fpr,train_fpr)),range(2),range(2))
sns.set(font_scale=1)#for label size
sns.heatmap(conf_matr_df_train_2,annot=True,annot_kws={"size":30},fmt='g')

Train confusion matrix
the maximum value of tpr*(1-fpr) 0.2499999780521262 for threshold 0.845
Out[164]:
<matplotlib.axes._subplots.AxesSubplot at 0x18813870fd0>
In [165]:
print("Test confusion matrix")
conf_matr_df_train_2=pd.DataFrame(confusion_matrix(y_test,predict(y_test_pred,tr_thresholds,test_fpr,test_fpr)),range(2),range(2))
sns.set(font_scale=1)#for label size
sns.heatmap(conf_matr_df_train_2,annot=True,annot_kws={"size":30},fmt='g')

Test confusion matrix
the maximum value of tpr*(1-fpr) 0.2499999593849979 for threshold 0.854
Out[165]:
<matplotlib.axes._subplots.AxesSubplot at 0x1881f49a4a8>

3. Conclusion

In [166]:
# Please compare all your mod# http://zetcode.com/python/prettytable/

from prettytable import PrettyTable
#If you get a ModuleNotFoundError error , install prettytable using: pip3 install prettytable
x=PrettyTable()
x.field_names=["Vectorizer","Hyper Parameter","AUC"]
x.add_row(["BOW",0.01,0.66])
x.add_row(["TFIDF",0.32,0.65])
x.add_row(["AVG W2V",1,0.69])
x.add_row(["TFIDF W2V",0.1,0.68])
x.add_row(["WITHOUT TEXT",0.01,0.57])
print(x)

+--------------+-----------------+------+
|  Vectorizer  | Hyper Parameter | AUC  |
+--------------+-----------------+------+
|     BOW      |       0.01      | 0.66 |
|    TFIDF     |       0.32      | 0.65 |
|   AVG W2V    |        1        | 0.69 |
|  TFIDF W2V   |       0.1       | 0.68 |
| WITHOUT TEXT |       0.01      | 0.57 |
+--------------+-----------------+------+

INFERENCE:

Here we can be observed that "Essays" and "Project Titles" play a major role in predicting the outcome of the project. So,we should not neglect them as the top four models containing them proved to have a better AUC score.

In [ ]: